Order 2184841: critical review

Journal of Economic Perspectives—Volume 18, Number 2—Spring 2004 —Pages 29 –50

Consumer Confidence and Consumer Spending

Sydney C. Ludvigson

A t least since the work of John Maynard Keynes, economists have ponderedthe ways in which consumer and investor sentiment—what Keynes (1936,Chapter 12) referred to as “animal spirits”—might influence the real economy. Today, the outcome of monthly consumer confidence surveys provides steady fodder for the business and financial press and is treated as an important piece of economic information. In the New York Times alone, more than 15 articles about consumer confidence and its potential impact on the economy appeared between July 2002 and June 2003. Consumer confidence is often cited by Federal Reserve Chairman Alan Greenspan as a key determinant of near-term economic growth (for example, Greenspan, 2002).

Despite the widespread attention given to surveys of consumer confidence, the mechanisms by which household attitudes influence the real economy are less well understood. Do consumer confidence surveys contain meaningful independent information about the economy, or do they simply repackage information already captured in other economic indicators? Do the surveys provide information about the future path of household spending, or do they reflect current or past events? Finally, do the surveys correspond neatly to any well-defined economic concept, or do they furnish only a nebulous barometer of household disposition?

This paper begins with an overview of how consumer confidence is measured and reported. It then evaluates what is known about the relationship between consumer attitudes and the real economy. The evidence suggests that the most popular survey measures do contain some information about the future path of

y Sydney C. Ludvigson is Assistant Professor of Economics, New York University, New York, New York, and Faculty Research Fellow, National Bureau of Economic Research, Cambridge, Massachusetts.

30 Journal of Economic Perspectives

aggregate consumer expenditure growth. However, much of that information can be found in other popular economic and financial indicators, and the independent information provided by consumer confidence predicts a relatively modest amount of additional variation in future consumer spending. Moreover, there is some evidence that consumer confidence surveys reflect expectations of income and non–stock market wealth growth, but evidence on the connection between these surveys and precautionary saving motives is mixed.

Measuring Consumer Attitudes: Survey Questions and Survey Components

The University of Michigan’s Consumer Sentiment Index and the Conference Board’s Consumer Confidence Index are the most widely followed measures of U.S. consumer confidence.1 Although the financial markets and the business commu- nity closely follow both indexes, much published academic research focuses on the Michigan index—most likely because of its longer time series. The Michigan index began as an annual survey in the late 1940s. In 1952, it was converted to a quarterly survey and in 1978 to a monthly survey. The Conference Board launched its index on a bimonthly basis in 1967 and expanded it to a monthly series in 1977. Figure 1 presents the basic time series of the two indices, with periods of recession shown by shaded areas. The two indexes broadly measure the same concept—public confidence in the economy— but they are based on different questions and some- times give conflicting signals. To interpret movements in these two series, it is important to understand some key differences in the specific questions that are asked as well as in sample size, survey methodology and index formulation.

Both the Conference Board and the University of Michigan base their overall index of consumer confidence on five questions that are part of a broader survey of consumer attitudes and expectations, shown in Exhibit 1. In both of the surveys, each of the five questions is given equal weight in the overall consumer confidence index. In addition to the overall index, both organizations report two component indexes: a present situation component and an expectations component.

Present Situation Component On each survey, two of the five questions ask respondents to assess present

economic conditions. The Conference Board’s present situation component takes a “snapshot” approach, asking respondents to evaluate current business conditions and job availability. Notice that the Conference Board survey asks specifically about

1 The material in this section closely reflects the discussion in Bram and Ludvigson (1998).

Sydney C. Ludvigson 31

job prospects in the respondent’s area. As a result, the Conference Board’s present situation component closely tracks labor market conditions like the nation’s un- employment rate and the growth in payroll employment. Michigan asks respon- dents to comment on the advisability of big-ticket household purchases and to assess changes in their own financial situation. Although this latter question is about the personal financial situation of the respondent, it does not directly ask about changes in the employment outlook. Thus, Michigan’s present conditions component is less closely tied to labor market conditions and tends to reflect recent changes in the economy rather than the level of economic activity.

These differences are reflected in the cyclical behavior of the two component indexes. As shown in Figure 2, Michigan’s present conditions component generally peaks in the early stages of economic recovery, when growth is high. By contrast, the Conference Board’s present situation component generally peaks in the late stages of economic expansion, when unemployment is low and the level of eco- nomic activity is high. The present conditions components of the two indexes are not closely correlated.

Expectations Component The three questions that ask about consumers’ expectations are fairly compa-

rable in the two surveys. The Conference Board survey asks about expected changes in business conditions, job availability and respondents’ income over the next six months. Because the Conference Board index includes a question about nominal income, it may overstate “confidence” during periods of high inflation. Michigan’s poses questions on expected business conditions— both over the next year and over the next five years—and expected changes in the respondent’s financial situation

Figure 1 Two Indexes of Consumer Attitudes

57 59 61 63 65

University of Michigan’s Index of Consumer sentiment

Left Scale

Conference Board’s Consumer confidence Index

Right Scale

67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 2001551953 50

60

70

80

90

100

110

120 Michigan Index, 1996: Q1 � 100 Conference Board Index, 1985 � 100

30

50

70

90

110

130

150

Sources: Conference Board; University of Michigan Survey Research Center. Note: Shaded areas denote periods designated recessions by the National Bureau of Economic Research.

32 Journal of Economic Perspectives

over the next year. This difference in time horizons appears to have little effect on response patterns and hence on index results.

The expectations components in the two surveys are highly correlated with each other, as shown in Figure 3. Moreover, Michigan’s present conditions and expectations components are much more closely correlated than are the Conference Board’s: the former exhibit a correlation of 0.82; the latter, a correlation of 0.40.

Exhibit 1 Component Questions of Consumer Confidence

Five questions make up the confidence indexes reported by the University of Michigan and the Conference Board. Each set of questions is part of a broader monthly survey of consumer attitudes. The questions have been renumbered and reordered here, compared to the way in which they are asked, to facilitate comparisons between the surveys. Also, the two surveys use slightly different terminology for the index component based on the first two questions; the discussion here will adopt the term present conditions for both organizations.

Michigan Survey Conference Board Survey

PRESENT CONDITIONS QUESTIONS PRESENT CONDITIONS QUESTIONS

Q1) Do you think now is a good or bad time for people to buy major household items? [good time to buy/uncertain, depends/bad time to buy]

Q1) How would you rate present general business conditions in your area? [good/normal/bad]

Q2) Would you say that you (and your family living there) are better off or worse off financially than you were a year ago? [better/ same/worse]

Q2) What would you say about available jobs in your area right now? [plentiful/not so many/ hard to get]

EXPECTATIONS QUESTIONS EXPECTATIONS QUESTIONS

Q3) Now turning to business conditions in the country as a whole—do you think that during the next twelve months, we’ll have good times financially or bad times or what? [good times/ uncertain/bad times]

Q3) Six months from now, do you think business conditions in your area will be [better/ same/worse]?

Q4) Looking ahead, which would you say is more likely—that in the country as a whole we’ll have continuous good times during the next five years or so or that we’ll have periods of widespread unemployment or depression, or what? [good times/uncertain/bad times]

Q4) Six months from now, do you think there will be [more/same/fewer] jobs available in your area?

Q5) Now looking ahead—do you think that a year from now, you (and your family living there) will be better off financially, or worse off, or just about the same as now? [better/same/ worse]

Q5) How would you guess your total family income to be six months from now? [higher/ same/lower]

Consumer Confidence and Consumer Spending 33

Sample Size and Survey Procedures The most important methodological differences between the two surveys

concern sample size, which affects sampling error and, thus, reliability, and index construction, which affects the range of movement in the indexes. The survey timing and release schedules also differ, which is an especially relevant consider- ation when conducting real-time analysis.

Michigan conducts its survey by phone throughout most of the month. Its sample size is approximately 500; a preliminary mid-month release is based on about two-thirds of the full sample phone interviews conducted early in the month. Final figures for the full sample are subsequently made available at the end of the month and are not subject to further revision. The Conference Board sends out a

Figure 2 Present Conditions of Consumer Attitudes

57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 2001551953 40

50

60

70

80

90

100

110

120 Michigan Index, 1996: Q1 � 100 Conference Board Index, 1985 � 100

0

20

40

60

80

100

120

140

160

180

Present conditions component of the Michigan Index

Left Scale

Present condition component of the Conference Board Index

Right Scale

Sources: Conference Board; University of Michigan Survey Research Center. Note: Shaded areas denote periods designated recessions by the National Bureau of Economic Research.

Figure 3 Expectations Component of Consumer Attitudes

57 59 61 63 65

Expectations component of the Michigan index

Left Scale

Expectations component of the Conference Board index

Right Scale

67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 2001551953 40

60

50

70

80

90

100

110

120 Michigan Index, 1996: Q1 � 100 Conference Board Index, 1985 � 100

0

20

40

60

80

100

120

140

Sources: Conference Board; University of Michigan Survey Research Center. Note: Shaded areas denote periods designated recessions by the National Bureau of Economic Research.

34 Journal of Economic Perspectives

mail survey at the end of the prior month, and responses flow in throughout the survey month. The effective sample size is roughly 3,500 (of a total mailing of 5,000). This sample of 5,000 is drawn from an original sample in which respondents agree to do the interviews. On the last Tuesday of the survey month, the Confer- ence Board formally releases its preliminary figures based on about 2,500 re- sponses. Final, revised data based on the full monthly sample are released with the next month’s preliminary figures and are not subject to further revision. Thus, Michigan’s preliminary figures are available earlier than the Conference Board’s.

Both surveys may be affected by sampling problems. The Michigan index is based on a relatively small sample size of roughly 500, with the preliminary mid-month release only two-thirds of that. This small sample size undoubtedly leads to noisy estimates of the population values. The larger the sample size the smaller the sampling error and therefore the narrower the statistical confidence interval for the estimated consumer confidence index. Curtin (2002) computes quantitative estimates of how much sampling error could be reduced by increasing the sample size of the Michigan survey. He concludes that tripling the sample from 500 interviews to 1,500 would reduce the 95 percent confidence interval for the Index of Consumer Sentiment from � 3.3 index points to � 1.9 index points, which is a sizable reduction. On the other hand, Curtin (2002) also reports that the prelim- inary survey results based on two-thirds of the full Michigan sample are very accurate, having a correlation of 0.99 with the final estimate.

The Conference Board survey has a larger sample, but— unlike the Michigan index—the overall response rate is unknown because only the response rate of households that previously agreed to participate in the survey is reported. This procedure makes it difficult to assess the representativeness of the Conference Board sample. A representative sample requires assigning each sample element a known probability of selection and on the absence of nonresponse bias. For the Michigan survey, Curtin, Presser and Singer (2000) report that the response rate ranged from a high of 72 percent to a low of 67 percent between 1979 and 1996, averaging about 70 percent; Curtin, Presser and Singer (2003) find that since 1996, the Michigan index has experienced much more substantial reductions in the response rate. The authors note that access to technology designed to screen unwanted calls has skyrocketed in recent years, a factor that has likely increased the nonresponse error in both surveys. Both surveys are also subject to nonsampling sources of error including reporting, editing and processing errors.

Constructing the Indexes The University of Michigan and the Conference Board use different method-

ologies to construct their indexes from the raw response data. The example in Table 1 illustrates how the Conference Board and Michigan would construct an index for one question using the same raw response data. For each index, the responses to the question can be categorized as positive, negative or neutral.

Sydney C. Ludvigson 35

Hypothetical figures are shown for two months along with the base-period levels against which the indexes are benchmarked.

Michigan would first calculate a “diffusion measure” by adding the difference between the positive and negative percentages, plus 100. Thus, for example, the current month’s value is 112 (100 � 24 – 12), and the prior month’s level is 120 (100 � 30 – 10). Next, an index is constructed by dividing the level of diffusion measure by the base-period level of 110 and then multiplying by 100. In the example below, this calculation yields a value of 101.8 (112 � 110 � 100) for the current month, down from the prior month’s level of 109.1 (120 � 110 � 100).

Using the same raw responses, the Conference Board would calculate its diffusion measure by dividing the positive response percentage by the sum of the positive and negative response percentages. This procedure gives a value of 66.7 (24 � (24 � 12) � 100) for the current month and 75 (30 � (30 � 10) � 100) for the prior month. Next, the index is calculated to be 106.7 (66.7 � 62.5 � 100) in the current month, down from a level of 120 (75.0 � 62.5 � 100) in the prior month—a drop of 13.3 points.

How are the question-level indexes aggregated to obtain an overall index? Michigan first averages the diffusion indexes into a composite diffusion index and then converts the results to a base-period index. Conversely, the Conference Board converts each diffusion index to a base-year index and then averages the indexes together.2 Because the Conference Board and Michigan use different base peri- ods—1985 and 1966:Q1, respectively—the response patterns on which the indexes

Table 1 Calculating the Two Indexes (example: calculating index levels from raw response data)

Base period Prior month Current month

Percentages of responses Positive 25 30 24 Neutral 60 60 64 Negative 15 10 12

Indicator level Michigan diffusion measure 110.0 120 112.0 Michigan index 100.0 109.1 101.8

Conference Board Diffusion measure 62.5 75.0 66.7 Conference Board index 100.0 120.0 106.7

2 Because the Conference Board’s diffusion measures are converted into base-year indexes before they are averaged arithmetically, a given question’s effective weight in the index is influenced by the selection of the base year. In theory, the choice of the base year could affect the magnitude and even the direction of change in the index. (The resulting problems are similar to those associated with the old fixed-base- year GDP deflator.) In practice, however, this feature has no discernible effect on the Conference

36 Journal of Economic Perspectives

are based may differ. As a result, the index levels of the two surveys are not comparable.

As a result of these methodological differences, the Conference Board’s overall index and component measures have a wider range of movement than Michigan’s; as the hypothetical example illustrates, identical shifts in the underlying responses tend to produce significantly larger moves in the Conference Board’s indexes than in Michigan’s. A good rule of thumb is that a one-point move in Michigan’s index is comparable to a two-point move in the Conference Board index. Because of differences in survey methodology, index construction and base year, index levels are not comparable; monthly changes must be compared on a standardized basis rather than in absolute terms. On a standardized basis, the Conference Board’s index is significantly less volatile than Michigan’s index.

Interpretation of the Indexes Two of the most common dilemmas in relying on consumer confidence as

an economic indicator are whether to focus on index-level or month-to-month changes and whether to focus on the present conditions or the expectations component. For the Conference Board index, it is particularly useful to exam- ine the present condition and expectations components individually. The level of the present conditions component serves as a good proxy for the level of economic activity, while the expectations component is more closely corre- lated with the rate of economic growth. For example, the present conditions component has a negative correlation of 0.88 with the unemployment rate, whereas the expectations component has a correlation with a four-quarter percent change in GDP growth of almost 0.60. On Michigan’s survey, both components are closely correlated and in general serve as an indicator of the pace of economic growth.

Do Consumer Confidence Surveys Predict Consumption?

It would hardly be surprising to find that confidence surveys reflect the current state of the economy. After all, most households should be aware of recent changes in their own economic situation. A more interesting question is whether the surveys provide information about the future path of household spending that is not already contained in other popular economic indicators. We begin by presenting regressions that forecast consumer expenditure using consumer confidence, a baseline set of economic indicators, and then combin-

Board’s index. Also, the Conference Board’s responses are seasonally adjusted, while Michigan’s are not. However, the seasonal adjustment has little effect, because neither index exhibits much seasonality.

Consumer Confidence and Consumer Spending 37

ing the two. We then discuss other research drawing connections between consumer confidence and consumption.

Regressions with Consumer Confidence and Consumption Table 2 presents how well the consumer confidence surveys can forecast

the growth of various categories of personal consumption expenditure. Following Bram and Ludvigson (1998), Table 2 measures the effect of con- sumer attitudes on five categories of household personal consumption expen- diture: total expenditure, motor vehicle expenditure, expenditure on all goods (excluding motor vehicles), expenditure on services and expenditure on durable goods excluding motor vehicles. The data, from the Bureau of Eco- nomic Analysis, span the period from the first quarter of 1968 to the fourth quarter of 2002; that is, the largest possible sample for which both indexes are available.

The independent variables included lags of consumer confidence over the previous four quarters. I do not report results for the present situation component because preliminary tests indicated that the expectations component of both indexes typically exhibited greater forecasting power. Previous research suggests that consumer sentiment may be linked to economic indicators largely because of unusually volatile movements in consumer attitudes during the Persian Gulf War and the 1990 –1991 recession (for example, Leeper, 1992). To control for this possibility, we also included a dummy variable set equal to one in the quarters corresponding to the 1990 –1991 recession.

For each category of consumption, Table 2 presents the adjusted R-squared statistic from a regression of consumption growth on the confi- dence measures. The p-values for the joint marginal significance of the lags of each variable, which appear in parentheses, give the probability that the explanatory variable can be excluded from the forecasting equa- tion.3 When the p-values are very low, the variables are statistically significant predictors of consumption growth.

Table 2 shows that measures of consumer attitudes on their own have both statistically and economically significant predictive power for quarterly consump- tion growth, in a variety of expenditure categories. Lagged values of both the Conference Board and Michigan overall index explain about 15 percent of the one-quarter-ahead variation in total personal consumption expenditure growth. The probability that this explanatory power was generated by chance is very small,

3 The growth in spending on durable goods may be positively autocorrelated, with the error term following a first-order moving-average process (Mankiw, 1982). Such first-order autocorrelation in the error term could cause the error term to be correlated with the one-period-lagged endogenous variable, a condition that could skew in-sample statistical tests of the joint marginal significance of the explanatory variables (the reported p-values). To address this problem, I explicitly model the error term, as an MA(1) process in the in-sample regressions.

38 Journal of Economic Perspectives

less than 1 percent. The expectations component of each index exhibits even more predictive power. For example, the Conference Board’s expectations component explains more than 20 percent of the variation in next quarter’s total consumer expenditure growth; similarly, Michigan’s expectation’s component explains 19 percent of the variation in next quarter’s expenditures on goods (excluding

Table 2 Forecasts of Consumption Growth by Consumer Confidence Indicators

Real personal consumption expenditure Michigan

index Conference

Board index Both

Overall index

Total 0.147 0.150 0.197 Conference Board — (0.008) (0.082) Michigan (0.002) — (0.040)

Motor vehicles 0.021 0.036 0.052 Conference Board — (0.109) (0.036) Michigan (0.066) — (0.067)

Goods, excluding motor vehicles 0.196 0.133 0.211 Conference Board — (0.011) (0.208) Michigan (0.001) — (0.003)

Services 0.096 0.137 0.137 Conference Board — (0.007) (0.008) Michigan (0.010) — (0.150)

Durables, excluding motor vehicles 0.174 0.084 0.231 Conference Board — (0.016) (0.002) Michigan (0.000) — (0.000)

Expectations component

Total 0.158 0.211 0.200 Conference Board — (0.000) (0.069) Michigan (0.002) — (0.593)

Motor vehicles 0.023 0.057 0.040 Conference Board — (0.006) (0.054) Michigan (0.040) — (0.453)

Goods, excluding motor vehicles 0.193 0.157 0.186 Conference Board — (0.024) (0.817) Michigan (0.002) — (0.016)

Services 0.098 0.207 0.197 Conference Board — (0.000) (0.000) Michigan (0.006) — (0.709)

Durables, excluding motor vehicles 0.153 0.081 0.146 Conference Board — (0.138) (0.526) Michigan (0.001) — (0.006)

Source: Author’s calculation. Notes: The table reports the adjusted R 2 statistic from the forecast of consumption growth with four lags of the confidence measures and a dummy variable for the 1990 –1991 recession; p-values for the joint marginal significance of the lags of the confidence measures appear in parentheses. Hypothesis tests were conducted using a heteroskedasticity and serial correlation robust covariance matrix. The sample covers the period from the first quarter of 1968 to the fourth quarter of 2002.

Sydney C. Ludvigson 39

motor vehicles). Thus, the evidence from in-sample regressions suggests that measures of consumer confidence—taken alone— have important predictive power for quarterly consumer expenditure growth. The next question is whether confi- dence measures contain predictive information that is not already contained in a standard set of baseline economic indicators.

Here, I use the same baseline indicators that are common in previous work on the predictive power of consumer confidence surveys, like Carroll, Fuhrer and Wilcox (1994) and Bram and Ludvigson (1998). The baseline indicators are lagged values of the dependent variable, labor income growth, the (log) first difference of the real stock price and the first difference of the three-month Treasury bill rate. Labor income is defined as wages and salaries plus transfers minus personal contribu- tions for social insurance, as it appears in the quarterly components from the Depart- ment of Commerce’s National Income and Product Accounts. Stock prices are quar- terly averages based on the Standard and Poor’s 500. The interest rate is the quarterly average based on the three-month Treasury bill rate, reported monthly by the Board of Governors of the Federal Reserve System. Nominal labor income and the Standard and Poor’s 500 index are deflated by the personal consumption expenditure implicit price deflator, as reported quarterly in the National Income and Product Accounts. Four lags of each of these variables are used in the benchmark regression.

The stock price and Treasury bill rate data are included because other researchers have argued that the information contained in consumer survey indicators should be assessed relative to that contained in financial indicators. For example, Leeper (1992) argues that consumer sentiment may have predictive power for spending because consumer surveys are made available on a more timely basis than other economic indicators such as income and consumption data. However, he notes that financial market indicators are available on an almost continuous basis and may contain much of the same information captured by consumer sentiment. Indeed, Leeper finds that consumer attitudes are only weakly correlated with variables such as unemployment and industrial production once financial indicators are included.

Table 3 reports the results of this baseline model, without consumer confidence indicators. For each category of consumption, the table presents the sum of the coefficients on the lags of each baseline variable and p-values for the joint marginal significance of the lags of each variable in parentheses. The sum of the coefficients on the four lags of each variable estimates the long-run effect of the variable on consump- tion growth. The long-run impact of most variables has the expected sign. Consump- tion growth is positively related to lagged consumption growth in every category of consumer expenditure studied, while lagged interest rates have a small negative effect on future consumption. Interestingly, the inclusion of the consumption and interest rate variables appears to reduce the statistical significance of the income and stock market variables in forecasting consumer expenditure growth on services, durable goods, excluding motor vehicles, and all goods, excluding motor vehicles.

To determine whether consumer sentiment contains additional information about future consumer spending, a measure of consumer confidence can be added

40 Journal of Economic Perspectives

to the baseline regression in Table 3. Consumer confidence is measured by either the Michigan or the Conference Board overall index, or the expectations compo- nent of each overall index. As before, I do not report results for the present situation component, but a dummy variable for the 1990 –1991 recession is in- cluded in the regression. Table 4 presents forecasting results from adding four lags of one of these consumer confidence measures to the baseline set of predictive variables for consumption growth. The table reports the increment to the adjusted R 2 that results from augmenting the baseline regression to include each of the attitudinal indicators. For example, if the increment to the adjusted R 2 from adding the four lags of some measure of confidence is X percent, the confidence augmented regression predicts about X percent more of the variation in the next quarter’s consumption growth than do the baseline predictive indicators. Again, the probability that the confidence indexes can be excluded from the forecasting equation appears in parentheses.

The results indicate that both the Michigan and Conference Board overall indexes have modest incremental forecasting power for total personal consumer expenditure growth. For the Michigan survey, the lagged values of consumer sentiment increase the adjusted R2 index by 5 percent, while adding the last four quarters of data from the Conference Board’s overall confidence index to the baseline equation predicts an additional 7 percent of the variation in the next quarter’s consumption growth. Including both indexes in the set of baseline indicators allows the regression to explain an additional 10 percent of the variation

Table 3 Baseline Forecast of Consumption Growth

Predicted variables Four lags of consumption

Four lags of income

Four lags of Treasury Bill rate

Four lags of S&P 500 R� 2

Total 0.76 0.02 �0.003 0.002 0.31 (0.000) (0.37) (0.002) (0.01)

Motor vehicles 0.56 0.15 �0.021 0.008 0.26 (0.02) (0.62) (0.022) (0.007)

Goods, excluding motor vehicles

0.55 0.15 �0.004 0.015 0.23

(0.003) (0.66) (0.027) (0.37) Services 0.97 0.01 �0.0004 �0.004 0.40

(0.000) (0.047) (0.000) (0.21) Durable goods, excluding

motor vehicles 0.79 0.21 �0.007 0.02 0.25

(0.000) (0.86) (0.000) (0.24)

Source: Author’s calculations. Notes: The table reports the sum of the coefficients on the lags of the variable indicated; the probability that the variable can be excluded from the prediction equation appears in parentheses. Hypothesis tests were conducted using a heteroskedasticity and serial correlation robust covariance matrix. The column labeled “R� 2” reports the adjusted R2 statistic for each regression. The sample covers the period from the first quarter of 1968 to the fourth quarter of 2002. S&P � Standard and Poor’s.

Consumer Confidence and Consumer Spending 41

Table 4 Forecast of Consumption Growth, Augmented by Consumer Confidence Indicators

Real personal consumption expenditure Michigan

index Conference Board

index Both

Overall index

Total 0.05 0.07 0.10 Conference Board — (0.004) (0.013) Michigan (0.012) — (0.026)

Motor vehicles �0.05 �0.06 0.09 Conference Board — (0.114) (0.001) Michigan (0.008) — (0.007)

Goods, excluding motor vehicles 0.03 0.05 0.05 Conference Board — (0.028) (0.046) Michigan (0.106) — (0.284)

Services �0.10 �0.08 0.01 Conference Board — (0.242) (0.000) Michigan (0.082) — (0.000)

Durables, excluding motor vehicles

0.02 0.04 0.03

Conference Board — (0.146) (0.109) Michigan (0.092) — (0.462)

Expectations component

Total 0.03 0.05 0.06 Conference Board — (0.087) (0.047) Michigan (0.046) — (0.201)

Motor vehicles 0.04 0.04 0.05 Conference Board — (0.044) (0.102) Michigan (0.018) — (0.055)

Goods, excluding motor vehicles 0.01 0.06 0.06 Conference Board — (0.038) (0.013) Michigan (0.257) — (0.177)

Services �0.10 �0.09 0.02 Conference Board — (0.143) (0.000) Michigan (0.117) — (0.001)

Durables, excluding motor vehicles

0.01 0.11 0.10

Conference Board — (0.233) (0.024) Michigan (0.172) — (0.359)

Source: Author’s calculations. Notes: The table reports the increment to the adjusted R 2 statistic from adding four lags of the confidence measures; p-values for the joint marginal significance of the lags of the confidence measures appear in parentheses. Hypothesis tests were conducted using a heteroskedasticity and serial correlation robust covariance matrix. The sample covers the period from the first quarter of 1968 to the fourth quarter of 2002.

42 Journal of Economic Perspectives

in next period’s total personal consumer expenditure growth.4 The bottom half of Table 4 shows the predictive power of each index’s measure of consumer expec- tations. Interestingly, the expectations index of each survey appears to have less forecasting power for total expenditure growth than does the overall index.

The results from the components of consumer spending are also mixed. For motor vehicle spending, the overall indexes taken individually actually weaken the predictive power of the baseline equation, but when both overall indexes are included, however, or when only the expectations component of either index is included, measures of consumer confidence have forecasting power for spend- ing on motor vehicles. For spending on services and durable goods (excluding motor vehicles), lagged values of either Michigan’s or the Conference Board’s overall index or their expectations component generally add little explanatory power to the consumption growth regressions. For services spending growth, the incremental adjusted R 2 is negative when either the Michigan or the Con- ference Board overall index is included, suggesting that including lagged mea- sures of consumer attitudes in the baseline model weakens its predictive capac- ity. The Conference Board index does help to forecast growth in the goods (excluding motor vehicles) category, but again the improvement is quite mod- est, although statistically significant. Neither the Michigan expectations component nor the Michigan overall index is a statistically significant marginal predictor of expenditure on goods (excluding motor vehicles), but when both the Michigan and Conference Board expectations component are included as predictive vari- ables, the fraction of next quarter’s expenditure growth that is explained rises by a fairly large 10 percent, with the Conference Board variables strongly statistically significant.

In summary, the results on the predictability of consumer attitudes for con- sumer spending are somewhat mixed. For total consumer expenditures, there is modest incremental information about the future path of spending in both the Michigan and Conference Board indexes, and including both surveys’ measure of expectations delivers fairly strong predictability of expenditure growth on goods (excluding motor vehicles). For other expenditure categories, however, the results are generally weaker, and for some categories of expenditure the inclusion of confidence indicators actually weakens the statistical relation between contempo- raneous indicators and future consumer spending.

Other Empirical Connections from Consumer Confidence to Consumption The analysis above uses historical data on consumption and income. Historical

data on consumption and income become available as advanced estimates only with

4 Although these regressions are the same as those carried out and reported in Bram and Ludvigson (1998), the results reported here differ because of the inclusion of new data. In a sample that ended in the third quarter of 1996, Bram and Ludvigson found that the Conference Board Index substantially outperformed the Michigan index in forecasting total consumer expenditures. Here, in data ending in the fourth quarter of 2002, the difference between the two indicators is found to be much more modest.

Sydney C. Ludvigson 43

a one-month delay, and are subsequently revised twice over the following two months, with occasional “benchmark revisions” done approximately every five years. It thus leaves open questions about whether consumer confidence might be useful as a real-time predictor.5

Some researchers have used historical data to explore the possibility that consumer confidence measures may improve forecasts because observations on sentiment for the first month of the quarter are available to predict the advanced estimate of the full-quarter value of consumption. Howrey (2001) and Slacalek (2003) recognize that this timing advantage of consumer confidence data is a benefit to real-time forecasters who are often charged with predicting the actual releases of consumer spending data. For example, if one wanted to forecast the advanced quarterly estimate of consumer expenditure growth from the Bureau of Economic Analysis, available with a one-month delay, one could use the contempo- raneous value of consumer confidence as a predictive variable, since it is available within the month. Slacalek (2003) finds that both measures of consumer confi- dence have statistically significant out-of-sample forecasting power for three-month consumption growth. Howrey (2001) also finds that the first-month value of Mich- igan’s Index of Consumer Sentiment is a statistically significant predictor of the full-quarter value of several categories of consumption growth, but once the values of personal consumption expenditure and disposable income for the first month of the current quarter are known, the statistical significance of the sentiment index disappears. Although this approach explicitly takes into account the timing advantage of consumer confidence indexes over other indicators in real-time-forecasting exer- cises, it still relies on historical data that has been revised, rather than real-time data.

To assess the real-time forecasting power of consumer surveys for consumption would require the construction of a real-time data set, a laborious task demanding tedious amalgamation of thousands of data points from the historical archives of data collection agencies. A real-time data set of several macroeconomic series has been amassed by researchers at the Federal Reserve Bank of Philadelphia and by Dean Croushore at the University of Virginia, but no formal statistical study of the real-time forecasting power of consumer confidence indexes has yet been completed.6

It is natural to ask if surveys of consumer attitudes forecast the spending of households actually surveyed, rather than merely capturing the broad economic trends in aggregate consumption data. To address this question, Souleles (2003)

5 One can simulate the real-time forecasting process using historical data by performing out-of-sample forecasts of consumer expenditure growth. For example, one could estimate the forecasting model on an initial subset of the whole sample and then perform a series of one-step-ahead forecasts. Such out-of-sample procedures are subject to caveats, however, because they are known to be considerably less powerful than in-sample procedures at detecting true predictability (Inoue and Kilian, 2002). 6 However, Lovell (2001) points out that the preliminary data errors have likely decreased over time, implying that the historical gap between real time data and the most recent revisions will overstate the errors in future preliminary observations.

44 Journal of Economic Perspectives

analyzed the household-level data that underlies the Michigan Index of Consumer Sentiment, a data set called the Michigan Survey of Consumer Attitudes and Behavior. The CAB contains the answers each household gave to the five questions that comprise the Michigan Index of Consumer Sentiment. To study how senti- ment is related to spending, Souleles uses the most comprehensive household-level data set on consumer expenditures, the Consumer Expenditure Survey, which also contains a rich set of household demographic indicators. Souleles links the household-level sentiment data with consumer spending by imputing the sentiment levels of households who participated in the Consumer Expenditure Survey from demographically similar households who participated in the survey of Consumer Attitudes and Behavior. Using the imputed values of sentiment, Souleles finds that consumer confidence is useful in forecasting the one-quarter-ahead consumption of individual households, even controlling for lagged consumption growth, other household characteristics and several macroeconomic variables.

Finally, other consumer survey questions may play a role in forecasting house- hold expenditure. Carroll and Dunn (1997) study the University of Michigan’s Unemployment Expectations Index, constructed by forming the fraction of con- sumers surveyed who thought unemployment would rise over the next twelve months minus the fraction who thought unemployment would fall. Carroll and Dunn find that higher unemployment expectations predict lower consumer spend- ing, even controlling for the movement in consumption that could be attributed to predictable movements in labor earnings.

What Economic Concept Does Consumer Confidence Measure?

Does the output of consumer confidence surveys correspond to any well- defined economic concept that would help explain the modest predictive power of consumer attitudes? This section considers two possible economic interpretations: that the indexes reflect precautionary savings motives or that the indexes capture household expectations of future income or wealth.

Consumer Confidence and Precautionary Saving If higher consumer confidence levels capture reduced uncertainty about

the future and therefore diminished the precautionary motive for saving, then higher consumer confidence should be associated with a higher level of con- sumption today, relative to tomorrow. All else equal, this means lower consump- tion growth going forward. Thus, if precautionary motives drive consumer sentiment, consumption growth measured from today to tomorrow should be negatively correlated with consumer sentiment today. This result is not what is found here, nor in previous studies like Carroll, Fuhrer and Wilcox (1994) or Bram and Ludvigson (1998). Instead, the sum of coefficients on the lagged consumer confidence measures in the forecasting regressions reported above is almost always greater than zero, indicating a positive rather than negative

Consumer Confidence and Consumer Spending 45

correlation between sentiment and future spending growth. Thus, a simple model of precautionary saving cannot explain the sentiment-spending correla- tion documented above.

Interestingly, one empirical study using micro data finds the opposite result. Using the Michigan Survey of Consumer Attitudes and Behavior (CAB), the household-level data that underlies the Michigan Index of Consumer Sentiment, Souleles (2003) reports that higher confidence is correlated with less saving (lower consumption growth), consistent with precautionary motives. It’s possible that the discrepancy between the micro-level and macro-level results is attributable to some sort of aggregation bias, but without a detailed study of the relation between the individual and aggregate survey responses, the possibility remains speculative.

Confidence and Expectations of Future Income or Wealth Another interpretation of consumer confidence surveys is that they primarily

capture household expectations of future income or wealth. Of course, under the general version of the permanent income hypothesis, consumption should change because of unexpected rises in permanent income. However, higher confidence levels could be related to future consumption growth if households are liquidity constrained so that greater income is closely tracked by greater consumption, or if some households follow a “rule-of-thumb” of consuming some fraction of their current income every period (Campbell and Mankiw, 1989).

To assess this interpretation, we should first investigate the possibility that confidence surveys forecast quarterly income and wealth growth. Table 5 reports the results of several forecasting regressions. The dependent variables are labor income growth (defined in the same way as in Table 2), the growth of wealth as measured by Federal Reserve data on stock market wealth or nonstock wealth;7 and the excess return on the Standard and Poor’s 500 stock market index, measured as returns to the Standard and Poor’s 500 index minus the three-month Treasury bill rate. The explanatory variables were four lags of either the Conference Board or Michigan measure of consumer confidence or consumer expectations, in each case controlling for lags in the dependent variable. Four lags each of the dependent variable were included in each regression.

As above, the table reports the increment to the adjusted R 2 statistic from

7 A complete description of the Federal Reserve wealth data may be found at �http://www. federalreserve.gov/release/Z1/Current/�. Briefly, total wealth is household net worth in billions of current dollars, measured at the end of the period. Stock market wealth includes direct household holdings, mutual fund holdings, holdings of private and public pension plans, personal trusts and insurance companies. Nonstock wealth includes tangible/real estate wealth, nonstock financial assets (all deposits, open market paper, U.S. Treasuries and Agency securities, municipal securities, corporate and foreign bonds and mortgages) and also includes ownership of privately traded companies in noncorporate equity and other. Subtracted off are liabilities, including mortgage loans and loans made under home equity lines of credit and secured by junior liens, installment consumer debt and other.

46 Journal of Economic Perspectives

Table 5 Forecast of Income Growth, Wealth Growth and Excess Return Augmented by Consumer Confidence Indicators

Predicted variable Michigan

index Conference Board

index Both R� 2

Overall index

Income growth 0.07 0.07 0.06 0.11 Conference Board — (0.004) (0.445) Michigan (0.002) — (0.410)

Stock wealth 0.004 0.03 0.04 �0.03 Conference Board — (0.175) (0.071) Michigan (0.302) — (0.337)

Nonstock wealth 0.05 0.03 0.04 0.24 Conference Board — (0.050) (0.413) Michigan (0.081) — (0.300)

Excess return 0.004 0.05 0.07 �0.01 Conference Board — (0.211) (0.047) Michigan (0.465) — (0.308)

Excess return 0.01 0.03 0.04 0.07 Conference Board — (0.422) (0.135) Michigan (0.384) — (0.257) CAY (0.000) (0.001) (0.001)

Expectation component

Income growth 0.07 0.12 0.10 0.11 Conference Board — (0.000) (0.027) Michigan (0.001) — (0.687)

Stock wealth 0.02 0.01 0.02 �0.03 Conference Board — (0.132) (0.108) Michigan (0.163) — (0.120)

Nonstock wealth 0.05 0.05 0.07 0.24 Conference Board — (0.006) (0.240) Michigan (0.035) — (0.183)

Excess return 0.02 0.03 0.05 �0.01 Conference Board — (0.097) (0.061) Michigan (0.313) — (0.126)

Excess return 0.01 0.02 0.04 0.07 Conference Board — (0.303) (0.116) Michigan (0.311) — (0.102) CAY (0.001) (0.002) (0.001)

Source: Author’s calculation. Notes: The table reports the increment to the adjusted R 2 statistic from adding four lags of the confidence measures for income growth, stock and nonstock wealth growth and excess return; p-values for the joint marginal significance of the lags of the confidence measures and CAY appear in paren- theses. Hypothesis tests were conducted using a heteroskedasticity and serial correlation robust covari- ance matrix. The column labeled “R� 2” reports the adjusted R 2 statistic for the predictive regression excluding consumer confidence. The sample covers the period from the first quarter of 1968 to the fourth quarter of 2002.

Sydney C. Ludvigson 47

including the lagged confidence measures to a regression of future income or wealth on lagged values of the dependent variable. The p-values for the joint marginal significance of the lags of each variable, which appear in parentheses, give the probability that the confidence measure can be excluded from the forecasting equation for income or wealth growth.

The results from Table 5 suggest that consumer confidence has some forecasting power for future labor income growth. The lagged values of both Michigan’s measure of consumer sentiment and the Conference Board’s mea- sure of consumer confidence increase the adjusted R 2 index by 7 percent, and the statistical probability that these variables have no predictive power for labor income growth is estimated to be less than 1 percent. By contrast, neither measure on its own has statistically significant predictive power for stock market wealth growth. Nevertheless, some evidence exists that consumer attitudes lead the stock market. When both the Michigan and Conference Board index are included in a predictive regression for the one-quarter-ahead excess return on the Standard and Poor’s 500 stock market index, the measures together explain about 7 percent of next quarter’s stock return and lags of the Conference Board measure are jointly statistically significant at better than the 5 percent level.

This result is not, however, robust to the inclusion of a proxy for the log consumption-wealth ratio, denoted CAY, shown in the last row of each panel.8 CAY is equal to the log of nondurables and services consumption minus a linear combination of the log of labor income and the log of household asset wealth. This variable can be thought of as a proxy for the log consumption-aggregate wealth ratio, where aggregate wealth includes human capital. Labor income is included as a proxy for the trend movements in unobservable human capital, and the linear combination of log labor income and log asset wealth serves as a proxy for the trend movements in log aggregate wealth. CAY has been found to be a strong forecaster of quarterly excess returns on the aggregate stock market (Lettau and Ludvigson, 2001). In this regression, CAY drives out the sentiment variables, implying that whatever information is in sentiment about future stock returns is better captured by the consumption-wealth proxy CAY.

Finally, both measures of consumer attitudes have some forecasting power for non–stock market wealth (primarily housing and nonstock financial wealth). The Conference Board and the Michigan expectations component, for example, pre- dict about 5 percent more of next quarter’s fluctuations in non–stock market wealth growth.

In summary, the results from Table 5 suggest that consumer confidence has

8 The coefficients of the linear combination of log labor income and log asset wealth in CAY are estimated using a dynamic least squares procedure. See Lettau and Ludvigson (2001, 2004) for details. CAY is not included as a control in the nonstock wealth regressions because it is has no forecasting power for future nonstock wealth (Lettau and Ludvigson, 2004).

48 Journal of Economic Perspectives

modest but statistically significant predictive power for future labor income growth and non–stock market wealth growth. Can this predictive power explain the fore- castability of consumption growth by measures of consumer attitudes? In other words, does consumer confidence matter for future consumption only because it predicts future income and wealth growth, which in turn determine future con- sumer spending growth?

For labor income, this question was addressed by Carroll, Fuhrer and Wilcox (1994). They used a two-stage approach. In the first stage, income and/or nonstock wealth growth can be predicted by lags of consumption growth, labor income growth, the Standard and Poor’s 500 price index return, a Treasury bill rate and one of the consumer confidence measures studied above. The forecasted values of labor income and nonstock wealth growth are saved. In the second stage, measures of consumption growth are regressed on the forecasted values of labor income growth and/or nonstock wealth growth, as well as the same lagged values of consumer confidence that were used in the first stage. If the consumer confidence measures only matter for future consumption because they forecast future labor income or wealth growth, they should display no incremental predictive power for consumption growth in the second stage regression.

However, Carroll, Fuhrer and Wilcox find that the forecasting power of consumer confidence cannot be entirely attributed to its predictive power for labor income growth. I updated their data on labor income to the present, carried out their calculations and also carried out a parallel exercise using data on nonstock wealth. The general pattern is that consumer sentiment again has predictive power for consumption growth above and beyond its ability to forecast labor income and/or nonstock wealth. In short, the information about expenditures in con- sumer attitudes is largely independent of the information in the predictable movements of household’s resources.

In summary, to the extent that measures of consumer attitudes have genuine forecasting power for consumption, the explanation for such a relation remains unclear. Measures of consumer confidence do forecast future changes in labor earnings and non–stock market wealth, but measures of consumer attitudes appear to be directly related to future consumption growth, not just indirectly through their predictive power for household income or wealth. Carroll, Fuhrer and Wilcox (1994) rule out other explanations for why sentiment forecasts aggregate spending growth, including a simple model of habit formation. The question of why con- sumer attitudes help predict future consumption growth remains a puzzle.

Concluding Remarks

The preceding investigation has focused only on the consumer spending- consumer confidence relation, but other researchers have explored the relation between consumer confidence and broader measures of economic activity. Mishkin (1978) focuses on the interrelation between household investment and

Consumer Confidence and Consumer Spending 49

consumer sentiment. Matsusaka and Sbordone (1995) find a relation between the Michigan Index of Consumer Sentiment and GDP growth. Of course, the results in these papers and those presented here raise the question of whether confidence measures serve mainly as proxies for some other fundamental variable that contributes to business cycle fluctuations. The difficulty with assessing this concern is that we don’t know what those other fundamentals might be. In assessing the incremental predictive power of consumer confi- dence measures, the best researchers can do is to rely on including, as control variables, those determinants of household spending that are both suggested by economic theory and empirically observable.

The discussion above has also focused on the question of whether consumer confidence forecasts consumption; much less research has been done on what causes movements in consumer confidence itself. For example, Fuhrer (1993) finds that 70 percent of the variation in Michigan’s Index of Consumer Sentiment can be explained by variation in national income, the unemployment rate, inflation and real interest rates. Nevertheless, Fuhrer also finds that some of the movement in consumer attitudes cannot be explained with broad economic aggregates. It is possible that there are more complex, possibly nonlinear, interactions between consumer confidence and economic variables, such as the stock market or unem- ployment. More work, both theoretical and empirical, is needed to understand the simultaneous relation between household attitudes and household spending.

y I am grateful to the Alfred P. Sloan Foundation, the National Science Foundation and the C.V. Starr Center for Applied Economics (NYU) for financial support, to Jason Bram, Richard Curtin and Alan Krueger for helpful comments and to Jinyong Kim for excellent research assistance.

References

Bram, Jason and Sydney C. Ludvigson. 1998. “Does Consumer Confidence Forecast House- hold Expenditure? A Sentiment Index Horse Race.” Federal Reserve Bank of New York: Economic Policy Review. June, 4:2, pp. 59 –78.

Campbell, John Y. and N. Gregory Mankiw. 1989. “Consumption, Income, and Interest Rates: Reinterpreting the Time Series Evi- dence,” in NBER Macroeconomics Annual. Oliver Blanchard and Stanly Fischer, eds. Cambridge: MIT Press, pp. 185–216.

Carroll, Christopher D. and Wendy E. Dunn. 1997. “Unemployment Expectations, Jumping (S,s) Triggers, and Household Expectations,” in

NBER Macroeconomics Annual. Benjamin S. Ber- nanke and Julio Rotemberg, eds. Cambridge: MIT Press, pp. 165–229.

Carroll, Christopher D., Jeffrey C. Fuhrer and David W. Wilcox. 1994. “Does Consumer Senti- ment Forecast Household Spending? If So Why?” American Economic Review. December, 84:5, pp. 1397– 408.

Curtin, Richard. 2002. “Surveys of Consumers: Theory, Methods, and Interpretation.” Paper written for the NABE 44th Annual Meeting, Sep- tember 30.

Curtin, Richard, Stanley Presser and Eleanor Singer. 2000. “The Effects of Response Rate

50 Journal of Economic Perspectives

Changes on the Index of Consumer Sentiment.” Public Opinion Quarterly. 64:4, pp. 413–28.

Curtin, Richard, Stanley Presser and Eleanor Singer. 2003. “Recent Response Rate Changes on the Michigan Survey of Consumer Attitudes.” Working paper, Survey Research Center, Univer- sity of Michigan.

Fuhrer, Jeffrey C. 1993. “What Role Does Consumer Sentiment Play in the U.S. Macro- economy?” New England Economic Review. January/February, pp. 32– 44.

Greenspan, Alan. 2002. “Remarks to the Bay Area Council Conference.” San Francisco, Cali- fornia, January 11; Available at �http://www. federalreserve.gov/boarddocs/speeches/2002/ 20020111/default.htm�.

Howrey, E. Philip. 2001. “The Predictive Power of the Index of Consumer Sentiment.” Brookings Papers on Economic Activity. 1, pp. 175–216.

Inoue, Atsushi and Lutz Kilian. 2002 “In- Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?” Working paper, University of Michigan.

Keynes, John Maynard. 1936. The General The- ory of Employment, Interest, and Money. New York: Hartcourt, Brace.

Leeper, Eric M. 1992. “Consumer Attitudes: King for a Day.” Federal Reserve Bank of Atlanta Economic Review. July/August, 77:3, pp. 1–16.

Lettau, Martin and Sydney C. Ludvigson. 2001. “Consumption, Aggregate Wealth, and Ex- pected Stock Returns.” Journal of Finance. 56:3, pp. 815– 49.

Lettau, Martin and Sydney C. Ludvigson. 2004. “Understanding Trend and Cycle in Asset Values: Reevaluating the Wealth Effect on Consumption.” American Economic Review. Forthcoming.

Lovell, Michael C. 2001. “Discussion of ‘The Predictive Power of the Index of Consumer Sen- timent.’” Brookings Papers on Economic Activity. 1, pp. 208 –13.

Mankiw, Gregory. 1982. “Hall’s Consumption Hypothesis and Durable Goods.” Journal of Mon- etary Economics. 10:3, pp. 417–25.

Matsusaka, John G. and Argia M. Sbordone. 1995. “Consumer Confidence and Economic Fluctuations.” Economic Enquiry. 33:2, pp. 296 – 318.

Mishkin, Frederic S. 1978. “Consumer Senti- ment and Spending on Durable Goods.” Brook- ings Papers on Economic Activity. 1, pp. 217–32.

Slacalek, Jiri. 2003. “Forecasting Consump- tion.” Working paper, Johns Hopkins University.

Souleles, Nicholas. 2003. “Expectations, Het- erogeneous Forecast Errors, and Consumption: Micro Evidence From the Michigan Consumer Sentiment Surveys.” Journal of Money, Credit, and Banking. Forthcoming.

This article has been cited by:

1. Zhifang He. 2020. Dynamic impacts of crude oil price on Chinese investor sentiment: Nonlinear causality and time-varying effect. International Review of Economics & Finance 66, 131-153. [Crossref]

2. Guillermo Acuña, Cristián Echeverría, Cristian Pinto-Gutiérrez. 2020. Consumer confidence and consumption: empirical evidence from Chile. International Review of Applied Economics 34:1, 75-93. [Crossref]

3. Walter Diegel, Alexandra Moritz, Joern H. Block, Antonia Botsari, Frank Lang, Helmut Krämer-Eis. Measuring Venture Capital Sentiment in Europe 137-174. [Crossref]

4. Jeroen Jonkman, Mark Boukes, Rens Vliegenthart. 2020. When Do Media Matter Most? A Study on the Relationship between Negative Economic News and Consumer Confidence across the Twenty- Eight EU States. The International Journal of Press/Politics 25:1, 76-95. [Crossref]

5. B. Öztürk, A.C.J. Stokman. 2019. Animal spirits and household spending in Europe and the US. Economics Letters 185, 108697. [Crossref]

6. Nazneen Ahmad, Sandeep Kumar Rangaraju. 2019. Macroeconomic effects of consumer confidence shock – evidence for state dependence. Journal of Economic Studies 46:7, 1293-1308. [Crossref]

7. George J. Jiang, H. Zafer Yüksel. 2019. Sentimental mutual fund flows. Financial Review 54:4, 709-738. [Crossref]

8. Solikin M. Juhro, Bernard Njindan Iyke. 2019. Consumer confidence and consumption expenditure in Indonesia. Economic Modelling . [Crossref]

9. David Ardia, Keven Bluteau, Kris Boudt. 2019. Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values. International Journal of Forecasting 35:4, 1370-1386. [Crossref]

10. Petar Sorić, Ivana Lolić, Oscar Claveria, Enric Monte, Salvador Torra. 2019. Unemployment expectations: A socio-demographic analysis of the effect of news. Labour Economics 60, 64-74. [Crossref]

11. Mahmoud Qadan. 2019. Risk appetite, idiosyncratic volatility and expected returns. International Review of Financial Analysis 65, 101372. [Crossref]

12. Zhifang He, Fangzhao Zhou, Xiaohua Xia, Fenghua Wen, Yiyuan Huang. 2019. Interaction between Oil Price and Investor Sentiment: Nonlinear Causality, Time- Varying Influence, and Asymmetric Effect. Emerging Markets Finance and Trade 55:12, 2756-2773. [Crossref]

13. Özer Arabacı, Metin Özdemir. 2019. TÜRKİYE’DE TÜKETİCİ GÜVENİ VE DÖVİZ KURU İLİŞKİSİ. TESAM Akademi Dergisi 11-28. [Crossref]

14. Joseph P. Byrne, Shuo Cao, Dimitris Korobilis. 2019. Decomposing global yield curve co-movement. Journal of Banking & Finance 106, 500-513. [Crossref]

15. Mali Chivakul, Bernhard Kassner. 2019. Can Consumption Growth in China Keep Up as Investment Slows?. Comparative Economic Studies 61:3, 381-412. [Crossref]

16. Masayuki Morikawa. 2019. Policy Uncertainty and Saving Attitude: Evidence from a Survey on Consumers. Journal of Consumer Affairs 53:3, 1297-1311. [Crossref]

17. Hamid Baghestani, Ajalavat Viriyavipart. 2019. Do factors influencing consumer home-buying attitudes explain output growth?. Journal of Economic Studies 46:5, 1104-1115. [Crossref]

18. Willem Vanlaer, Samantha Bielen, Wim Marneffe. 2019. Consumer Confidence and Household Saving Behaviors: A Cross-Country Empirical Analysis. Social Indicators Research 104. . [Crossref]

19. Lasse Bork, Stig V. Møller, Thomas Q. Pedersen. 2019. A New Index of Housing Sentiment. Management Science . [Crossref]

20. Sai-wei LI, Chen ZHU, Qi-hui CHEN, Yu-mei LIU. 2019. Consumer confidence and consumers' preferences for infant formulas in China. Journal of Integrative Agriculture 18:8, 1793-1803. [Crossref]

21. Baptiste Massenot, Yuri Pettinicchi. 2019. Can households see into the future? Survey evidence from the Netherlands. Journal of Economic Behavior & Organization 164, 77-90. [Crossref]

22. Yuliya Demyanyk, Dmytro Hryshko, María José Luengo‐Prado, Bent E. Sørensen. 2019. The Rise and Fall of Consumption in the 2000s: A Tangled Tale. Economica 86:343, 495-531. [Crossref]

23. Michael Tang, Li Yao. 2019. The Impact of Economy-Wide Sentiment on Analysts’ Research Activities. Journal of Accounting, Auditing & Finance 34:3, 385-410. [Crossref]

24. Mahmoud Qadan, David Y. Aharon. 2019. Can investor sentiment predict the size premium?. International Review of Financial Analysis 63, 10-26. [Crossref]

25. Hashmat Khan, Santosh Upadhayaya. 2019. Does business confidence matter for investment?. Empirical Economics 28. . [Crossref]

26. Han Chen, Rui Chen, Shaniel Bernard, Imran Rahman. 2019. US hotel industry revenue: an ARDL bounds testing approach. International Journal of Contemporary Hospitality Management 31:4, 1720-1743. [Crossref]

27. Minchae Song, Kyung‐shik Shin. 2019. Forecasting economic indicators using a consumer sentiment index: Survey‐based versus text‐based data. Journal of Forecasting 33. . [Crossref]

28. Alan Collins, Adam Cox, Renatas Kizys, Frederick Haynes, Sarah Machin, Benjamin Sampson. 2019. Suicide, sentiment and crisis. The Social Science Journal . [Crossref]

29. Joseph P. Byrne, Marco Lorusso, Bing Xu. 2019. Oil prices, fundamentals and expectations. Energy Economics 79, 59-75. [Crossref]

30. Javier Rojo Suárez, Ana Belén Alonso Conde, Ricardo Ferrero Pozo. 2019. European equity markets: Who is the truly representative investor?. The Quarterly Review of Economics and Finance . [Crossref]

31. Jess Benhabib, Mark M Spiegel. 2019. Sentiments and Economic Activity: Evidence from US States. The Economic Journal 129:618, 715-733. [Crossref]

32. Megan Doherty Bea. 2019. Racial and Ethnic Differences in Consumers’ Economic Expectations. Socius: Sociological Research for a Dynamic World 5, 237802311881527. [Crossref]

33. Andres Algaba, David Ardia, Keven Bluteau, Samuel Borms, Kris Boudt. 2019. Econometrics Meets Sentiment: An Overview of Methodology and Applications. SSRN Electronic Journal . [Crossref]

34. Myroslav Pidkuyko. 2019. Consumer Sentiment, Durable Consumption, and Stock Returns. SSRN Electronic Journal . [Crossref]

35. Ben Zhe Wang. 2019. The Impact of Fiscal Budget Deficit on Consumer Sentiment: News from Perceived News. SSRN Electronic Journal . [Crossref]

36. Helen X. H. Bao, Haotong Li. 2019. Housing Wealth and Residential Energy Consumption in the UK. SSRN Electronic Journal . [Crossref]

37. Daragh Clancy, Lorenzo Ricci. 2019. Loss Aversion, Economic Sentiments and International Consumption Smoothing. SSRN Electronic Journal . [Crossref]

38. Erik Kole, Liesbeth Noordegraaf-Eelens, Bas Vringer. 2019. Cognitive Biases and Consumer Sentiment. SSRN Electronic Journal . [Crossref]

39. Daniel Borup, Erik Christian Montes Schütte. 2019. Asset Pricing With Data Revisions. SSRN Electronic Journal . [Crossref]

40. Daniel Navarro-Martinez, Graham Loomes, Andrea Isoni, David Butler, Larbi Alaoui. 2018. Boundedly rational expected utility theory. Journal of Risk and Uncertainty 57:3, 199-223. [Crossref]

41. Abel Brodeur. 2018. The Effect of Terrorism on Employment and Consumer Sentiment: Evidence from Successful and Failed Terror Attacks. American Economic Journal: Applied Economics 10:4, 246-282. [Abstract] [View PDF article] [PDF with links]

42. Christian Gillitzer, Nalini Prasad. 2018. The Effect of Consumer Sentiment on Consumption: Cross-Sectional Evidence from Elections. American Economic Journal: Macroeconomics 10:4, 234-269. [Abstract] [View PDF article] [PDF with links]

43. Miia Parnaudeau, Jean-Louis Bertrand. 2018. The contribution of weather variability to economic sectors. Applied Economics 50:43, 4632-4649. [Crossref]

44. Tong Suk Kim, Heewoo Park. 2018. Is stock return predictability of option-implied skewness affected by the market state?. Journal of Futures Markets 38:9, 1024-1042. [Crossref]

45. Hatice Gökçe Karasoy Can, Çağlar Yüncüler. 2018. The Explanatory Power and the Forecast Performance of Consumer Confidence Indices for Private Consumption Growth in Turkey. Emerging Markets Finance and Trade 54:9, 2136-2152. [Crossref]

46. Byungoh Kim, Sangwon Suh. 2018. Sentiment-based momentum strategy. International Review of Financial Analysis 58, 52-68. [Crossref]

47. Steven Ongena, Anna (Ania) Zalewska. 2018. Institutional and individual investors: Saving for old age. Journal of Banking & Finance 92, 257-268. [Crossref]

48. Blanka Škrabić Perić, Petar Sorić. 2018. A Note on the “Economic Policy Uncertainty Index”. Social Indicators Research 137:2, 505-526. [Crossref]

49. Kim Abildgren, Niels Lynggård Hansen, Andreas Kuchler. 2018. Overoptimism and house price bubbles. Journal of Macroeconomics 56, 1-14. [Crossref]

50. Aaron H. Anglin, Aaron F. McKenny, Jeremy C. Short. 2018. The Impact of Collective Optimism on New Venture Creation and Growth: A Social Contagion Perspective. Entrepreneurship Theory and Practice 42:3, 390-425. [Crossref]

51. Hassan F. Gholipour, Reza Tajaddini. 2018. Sub-dimensions of consumer confidence and outgoing tourist expenditures. Anatolia 84, 1-11. [Crossref]

52. James J. Angel, Douglas M. McCabe. 2018. Insider Trading 2.0? The Ethics of Information Sales. Journal of Business Ethics 147:4, 747-760. [Crossref]

53. Víctor Leiva, Camilo Lillo, Rodrigo Morrás. On a Business Confidence Index and Its Data Analytics: A Chilean Case 67-85. [Crossref]

54. Mahmoud Qadan, Hazar Nama. 2018. Investor sentiment and the price of oil. Energy Economics 69, 42-58. [Crossref]

55. Marcel Garz. 2018. Effects of unemployment news on economic perceptions – Evidence from German Federal States. Regional Science and Urban Economics 68, 172-190. [Crossref]

56. Shahid Shayaa, Sulaiman Ainin, Noor Ismawati Jaafar, Shamsul Bahri Zakaria, Seuk Wai Phoong, Wai Chung Yeong, Mohammed Ali Al-Garadi, Ashraf Muhammad, Arsalan Zahid Piprani. 2018. Linking consumer confidence index and social media sentiment analysis. Cogent Business & Management 5:1. . [Crossref]

57. Allen N. Berger, Felix Irresberger, Raluca A. Roman. 2018. Do Small Banks Alleviate Householdss Financial Constraints? Some Surprising Evidence From University of Michigan Surveys of Consumers. SSRN Electronic Journal . [Crossref]

58. Huson Joher Ali Ahmed. 2018. Firm Leverage Policy Decisions: Does Consumer Behaviour Explain Sectoral-Level Corporate Debt Policy in the U.S?. SSRN Electronic Journal . [Crossref]

59. Baptiste Massenot, Yuri Pettinicchi. 2018. Can Households See into the Future? Survey Evidence from the Netherlands. SSRN Electronic Journal . [Crossref]

60. Kerri Agnew. 2018. Breaking for the Border: The Effect of the Exchange Rate on Crossborder Shopping. SSRN Electronic Journal . [Crossref]

61. 서서서. 2017. Analysis on Factors Influencing Consumer Survey Index in Korea. The Journal of Eurasian Studies 14:4, 159-178. [Crossref]

62. Bas van Aarle, Cindy Moons. 2017. Sentiment and Uncertainty Fluctuations and Their Effects on the Euro Area Business Cycle. Journal of Business Cycle Research 13:2, 225-251. [Crossref]

63. Stanislao Gualdi, Marco Tarzia, Francesco Zamponi, Jean-Philippe Bouchaud. 2017. Monetary policy and dark corners in a stylized agent-based model. Journal of Economic Interaction and Coordination 12:3, 507-537. [Crossref]

64. Rene Segers, Philip Hans Franses, Bert de Bruijn. 2017. A novel approach to measuring consumer confidence. Econometrics and Statistics 4, 121-129. [Crossref]

65. Stéphane Dees. 2017. The role of confidence shocks in business cycles and their global dimension. International Economics 151, 48-65. [Crossref]

66. Ellen M. Key, Kathleen M. Donovan. 2017. The Political Economy: Political Attitudes and Economic Behavior. Political Behavior 39:3, 763-786. [Crossref]

67. Aneta Maria Kłopocka. 2017. Does Consumer Confidence Forecast Household Saving and Borrowing Behavior? Evidence for Poland. Social Indicators Research 133:2, 693-717. [Crossref]

68. Hugh M. J. Colaco, Amedeo De Cesari, Shantaram P. Hegde. 2017. Retail Investor Attention and IPO Valuation. European Financial Management 23:4, 691-727. [Crossref]

69. Jun Sik Kim, Da-Hea Kim, Sung Won Seo. 2017. Investor Sentiment and Return Predictability of the Option to Stock Volume Ratio. Financial Management 46:3, 767-796. [Crossref]

70. ZHAOCHEN HE. 2017. The Contractionary Effect of Bad Economic News. Journal of Money, Credit and Banking 49:6, 1339-1384. [Crossref]

71. MICHELLE L. BARNES, GIOVANNI P. OLIVEI. 2017. Consumer Attitudes and Their Forecasting Power for Consumer Spending. Journal of Money, Credit and Banking 49:5, 1031-1058. [Crossref]

72. Cristian Huse, Nikita Koptyug. 2017. Bailing on the Car That Was Not Bailed Out: Bounding Consumer Reactions to Financial Distress. Journal of Economics & Management Strategy 26:2, 337-374. [Crossref]

73. Scott A. Crossley, Kristopher Kyle, Danielle S. McNamara. 2017. Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis. Behavior Research Methods 49:3, 803-821. [Crossref]

74. Shu Feng, Na Wang, Edward J. Zychowicz. 2017. Sentiment and the Performance of Technical Indicators. The Journal of Portfolio Management 43:3, 112-125. [Crossref]

75. Lai Van VO, Huong Thi Thu LE, Danh Vinh LE, Minh Tuan PHUNG, Yi-Hsien WANG, Fu-Ju YANG. 2017. CUSTOMER SATISFACTION AND CORPORATE INVESTMENT POLICIES. Journal of Business Economics and Management 18:2, 202-223. [Crossref]

76. Junyan Shen, Jianfeng Yu, Shen Zhao. 2017. Investor sentiment and economic forces. Journal of Monetary Economics 86, 1-21. [Crossref]

77. Wändi Bruine de Bruin, Wilbert van der Klaauw, Maarten van Rooij, Federica Teppa, Klaas de Vos. 2017. Measuring expectations of inflation: Effects of survey mode, wording, and opportunities to revise. Journal of Economic Psychology 59, 45-58. [Crossref]

78. Nikolaos Sariannidis, Grigoris Giannarakis, Xanthi Partalidou, Bakas Evangelos. 2017. The effect of Dow Jones Sustainability Index on Consumer Sentiment Index. Investment Management and Financial Innovations 14:1, 89-95. [Crossref]

79. Inho Park, Tae Yoon Kim. 2017. Measuring economic sentiment using ordinary response options. Communications for Statistical Applications and Methods 24:2, 163-172. [Crossref]

80. Alastair Lawrence, James P. Ryans, Estelle Y. Sun. 2017. Investor Demand for Sell-Side Research. The Accounting Review 92:2, 123-149. [Crossref]

81. Hassan Gholipour Fereidouni, Reza Tajaddini. 2017. Housing Wealth, Financial Wealth and Consumption Expenditure: The Role of Consumer Confidence. The Journal of Real Estate Finance and Economics 54:2, 216-236. [Crossref]

82. Cesario Mateus, Raju Chinthalapati, Irina B. Mateus. 2017. Intraday industry-specific spillover effect in European equity markets. The Quarterly Review of Economics and Finance 63, 278-298. [Crossref]

83. Zhi Da, Dayong Huang, Hayong Yun. 2017. Industrial Electricity Usage and Stock Returns. Journal of Financial and Quantitative Analysis 52:1, 37-69. [Crossref]

84. Jorge Ridderstaat, Robertico Croes. 2017. The Link between Money Supply and Tourism Demand Cycles. Journal of Travel Research 56:2, 187-205. [Crossref]

85. Filip Premik, Ewa Stanisławska. 2017. The Impact of Inflation Expectations on Polish Consumers’ Spending and Saving. Eastern European Economics 55:1, 3-28. [Crossref]

86. David Ardia, Keven Bluteau, Kris Boudt. 2017. Aggregating the Panel of Daily Textual Sentiment for Sparse Forecasting of Economic Growth. SSRN Electronic Journal . [Crossref]

87. Ayben Koy, Murat Akkaya. 2017. The Role of Consumer Confidence as a Leading Indicator on Stock Returns: A Markov Switching Approach. SSRN Electronic Journal . [Crossref]

88. Joseph P. Byrne, Marco Lorusso, Bing Xu. 2017. Oil Prices and Informational Frictions: The Time- Varying Impact of Fundamentals and Expectations. SSRN Electronic Journal . [Crossref]

89. Donja Darai, Shimon Kogan, Anthony M. Kwasnica, Roberto A. Weber. 2017. Aggregate Sentiment and Investment: An Experimental Study. SSRN Electronic Journal . [Crossref]

90. Haiyang Zhang. 2017. Incorporating Google Trends Data in Predicting Consumer Confidence in Sri Lanka. SSRN Electronic Journal . [Crossref]

91. Frankie Chau, Rataporn Deesomsak, Dimitrios Koutmos. 2016. Does investor sentiment really matter?. International Review of Financial Analysis 48, 221-232. [Crossref]

92. M. Iqbal Ahmed, Steven P. Cassou. 2016. Does consumer confidence affect durable goods spending during bad and good economic times equally?. Journal of Macroeconomics 50, 86-97. [Crossref]

93. Christos Kallandranis,, Socrates Karidis. 2016. “Voting with their Feet” in Times of Crisis: The Case of Southern Europe. Applied Economics Quarterly 62:4, 321-341. [Crossref]

94. Kajal Lahiri, George Monokroussos, Yongchen Zhao. 2016. Forecasting Consumption: the Role of Consumer Confidence in Real Time with many Predictors. Journal of Applied Econometrics 31:7, 1254-1275. [Crossref]

95. Maurizio Bovi. 2016. The tale of two expectations. Quality & Quantity 50:6, 2677-2705. [Crossref] 96. Marta Lachowska. 2016. Expenditure and confidence: using daily data to identify shocks to consumer

confidence. Oxford Economic Papers 68:4, 920-944. [Crossref] 97. Siqi Zheng, Weizeng Sun, Matthew E. Kahn. 2016. Investor Confidence as a Determinant of China's

Urban Housing Market Dynamics. Real Estate Economics 44:4, 814-845. [Crossref] 98. Mina Dragouni, George Filis, Konstantinos Gavriilidis, Daniel Santamaria. 2016. Sentiment, mood

and outbound tourism demand. Annals of Tourism Research 60, 80-96. [Crossref] 99. Edoardo Lozza, Andrea Bonanomi, Cinzia Castiglioni, A. Claudio Bosio. 2016. Consumer Sentiment

after the Global Financial Crisis. International Journal of Market Research 58:5, 671-691. [Crossref] 100. Hamid Baghestani, Michael Malcolm. 2016. Factors predicting the US birth rate. Journal of Economic

Studies 43:3, 432-446. [Crossref]

101. Jonathan Z. Berman, An T.K. Tran, John G. Lynch, Gal Zauberman. 2016. Expense Neglect in Forecasting Personal Finances. Journal of Marketing Research 53:4, 535-550. [Crossref]

102. Shiu-Sheng Chen, Yu-Hsi Chou. 2016. DOES FEAR LEAD TO RECESSIONS?. Macroeconomic Dynamics 20:5, 1247-1263. [Crossref]

103. Ioannis Chatziantoniou, Stavros Degiannakis, Bruno Eeckels, George Filis. 2016. Forecasting tourist arrivals using origin country macroeconomics. Applied Economics 48:27, 2571-2585. [Crossref]

104. Tirtha Dhar, Charles B. Weinberg. 2016. Measurement of interactions in non-linear marketing models: The effect of critics' ratings and consumer sentiment on movie demand. International Journal of Research in Marketing 33:2, 392-408. [Crossref]

105. Radek Tahal, Zuzana Chytková, Marek Novinský. 2016. The Effect of Socioeconomic Classes on the Subjective Perception of Economic Situation. Studia Commercialia Bratislavensia 9:33, 102-112. [Crossref]

106. Mustafa Caglayan, Bing Xu. 2016. Sentiment volatility and bank lending behavior. International Review of Financial Analysis 45, 107-120. [Crossref]

107. Saravanan Kesavan, Tarun Kushwaha, Vishal Gaur. 2016. Do High and Low Inventory Turnover Retailers Respond Differently to Demand Shocks?. Manufacturing & Service Operations Management 18:2, 198-215. [Crossref]

108. Robert Gausden, Mohammad S. Hasan. 2016. Would information on consumer confidence have helped to predict UK household expenditure during the recent economic crisis?. Applied Economics 48:18, 1695-1709. [Crossref]

109. Jair N. Ojeda-Joya, Julián A. Parra-Polanía, Carmiña O. Vargas. 2016. Fiscal rules as a response to commodity shocks: A welfare analysis of the Colombian scenario. Economic Modelling 52, 859-866. [Crossref]

110. Johnson Kakeu, Sharri Byron. 2016. Optimistic about the future? How uncertainty and expectations about future consumption prospects affect optimal consumer behavior. The B.E. Journal of Macroeconomics 16:1. . [Crossref]

111. WWndi Bruine de Bruin, Wilbert van der Klaauw, Maarten <!>van Rooij, Federica Teppa, Klaas de Vos. 2016. Measuring Expectations of Inflation: Effects of Survey Mode, Wording, and Opportunities to Revise. SSRN Electronic Journal . [Crossref]

112. Saeed Heravi, Joshy Easaw, Roberto Golinelli. 2016. Generalized State-Dependent Models: A Multivariate Approach. SSRN Electronic Journal . [Crossref]

113. Lai Van Vo, Huong T. T. Le, Tuan Minh Phung, Yi-Hsien Wang, Fu-Ju Yang. 2016. Customer Satisfaction and Corporate Investment Policies. SSRN Electronic Journal . [Crossref]

114. Marcio Genovevo da Costa. 2016. Determinants of IPO Activity: Does IPO Activity Fluctuate in Accordance with the Business Cycle? Evidence from European Firms. SSRN Electronic Journal . [Crossref]

115. Ahmet Kose, Murat Akkaya. 2016. Beklenti Ve GGven Anketlerinin Finansal Piyasalara Etkisi: Bist 100 zerine Bir Uygulama (The Effects of Sentiment and Confidence Indices on Financial Markets: An Application on BIST 100). SSRN Electronic Journal . [Crossref]

116. Gabe J. de Bondt, Stefano Schiaffi. 2015. Confidence Matters for Current Economic Growth: Empirical Evidence for the Euro Area and the United States*. Social Science Quarterly 96:4, 1027-1040. [Crossref]

117. Bengt Assarsson,, Pär Österholm. 2015. Do Swedish Consumer Confidence Indicators Do What They Are Intended to Do?. Applied Economics Quarterly 61:4, 391-404. [Crossref]

118. Konstantinos Drakos, Christos Kallandranis. 2015. A Note on the Effect of Terrorism on Economic Sentiment. Defence and Peace Economics 26:6, 600-608. [Crossref]

119. Magda Kandil. 2015. Determinants of Lower Saving Rates in the USA: Prospects and Implications. International Journal of Finance & Economics 20:4, 328-340. [Crossref]

120. Don Weatherburn, Patricia Menéndez, Fernando Tusell. 2015. Postulation is no substitute for evidence: a rejoinder to Shepherd & Page. Addiction 110:10, 1584-1585. [Crossref]

121. Ming-Hsiang Chen. 2015. Understanding the impact of changes in consumer confidence on hotel stock performance in Taiwan. International Journal of Hospitality Management 50, 55-65. [Crossref]

122. Auke Hunneman, Peter C. Verhoef, Laurens M. Sloot. 2015. The Impact of Consumer Confidence on Store Satisfaction and Share of Wallet Formation. Journal of Retailing 91:3, 516-532. [Crossref]

123. Roel Beetsma, Jacopo Cimadomo, Oana Furtuna, Massimo Giuliodori. 2015. The confidence effects of fiscal consolidations. Economic Policy 30:83, 439-489. [Crossref]

124. Song-Yi Youn, Kyu-Hye Lee. 2015. Self-image and Fashion Leadership: Focusing on Clothing Involvement and Consumer Confidence. Fashion & Textile Research Journal 17:3, 382-391. [Crossref]

125. Stephen Bruestle, W. Mark Crain. 2015. A mean-variance approach to forecasting with the consumer confidence index. Applied Economics 47:23, 2430-2444. [Crossref]

126. P. M. Kellstedt, S. Linn, A. L. Hannah. 2015. The Usefulness of Consumer Sentiment: Assessing Construct and Measurement. Public Opinion Quarterly 79:1, 181-203. [Crossref]

127. Dashan Huang, Fuwei Jiang, Jun Tu, Guofu Zhou. 2015. Investor Sentiment Aligned: A Powerful Predictor of Stock Returns. Review of Financial Studies 28:3, 791-837. [Crossref]

128. María-Isabel Martínez-Serna, Eliseo Navarro. 2015. Interest Rate Volatility and Business Cycle Expectations. International Finance 18:1, 69-92. [Crossref]

129. DAVID C. LING, JOSEPH T.L. OOI, THAO T.T. LE. 2015. Explaining House Price Dynamics: Isolating the Role of Nonfundamentals. Journal of Money, Credit and Banking 47:S1, 87-125. [Crossref]

130. Jing Jian Xiao. Consumer Spending 151-180. [Crossref] 131. Aminu Muhammad, Nirmalie Wiratunga, Robert Lothian. Context-Aware Sentiment Analysis of

Social Media 87-104. [Crossref] 132. Matthias Eickhoff. Enabling Reproducible Sentiment Analysis: A Hybrid Domain-Portable

Framework for Sentiment Classification 215-229. [Crossref] 133. Stig V. Møller, Jesper Rangvid. 2015. End-of-the-year economic growth and time-varying expected

returns. Journal of Financial Economics 115:1, 136-154. [Crossref] 134. Y. K. Kim, M. Setterfield, Y. Mei. 2015. Aggregate consumption and debt accumulation: an empirical

examination of US household behaviour. Cambridge Journal of Economics 39:1, 93-112. [Crossref] 135. Christian Gillitzer, Nalini Prasad. 2015. The Effect of Consumer Sentiment on Consumption: Cross-

Sectional Evidence from Elections. SSRN Electronic Journal . [Crossref] 136. Chao Ma. 2015. House Price Expectations and Mortgage Default Decisions. SSRN Electronic Journal

. [Crossref] 137. Cristian Huse, Nikita Koptyug. 2015. Bailing on the Car That Wasn't Bailed Out: Bounding

Consumer Reactions to Financial Distress. SSRN Electronic Journal . [Crossref] 138. Tonmoy Chatterjee, Soumyananda Dinda. Consumer Sentiment and Confidence during Post-Crisis

2008 44-61. [Crossref] 139. Wai-Yip Alex Ho, James Yetman. 2014. Do economies stall?. Applied Economics 46:35, 4267-4275.

[Crossref] 140. Frankie Chau, Rataporn Deesomsak. 2014. Does linkage fuel the fire? The transmission of financial

stress across the markets. International Review of Financial Analysis 36, 57-70. [Crossref]

141. Charlotte Christiansen, Jonas Nygaard Eriksen, Stig Vinther Møller. 2014. Forecasting US recessions: The role of sentiment. Journal of Banking & Finance 49, 459-468. [Crossref]

142. Véronique Flambard, Nicolas Vaillant. 2014. Consumer sentiment and government confidence: Is there a stable causal relationship?. French Politics 12:4, 310-330. [Crossref]

143. Dominic Abrams, Milica Vasiljevic. 2014. How Does Macroeconomic Change Affect Social Identity (and Vice Versa?): Insights from the European Context. Analyses of Social Issues and Public Policy 14:1, 311-338. [Crossref]

144. Cetin Ciner. 2014. The Time Varying Relation Between Consumer Confidence and Equities. Journal of Behavioral Finance 15:4, 312-317. [Crossref]

145. Stefano Gubellini. 2014. Conditioning information and cross-sectional anomalies. Review of Quantitative Finance and Accounting 43:3, 529-569. [Crossref]

146. Wai Mun Fong, Benjamin Toh. 2014. Investor sentiment and the MAX effect. Journal of Banking & Finance 46, 190-201. [Crossref]

147. Stig V. Møller, Henrik Nørholm, Jesper Rangvid. 2014. Consumer confidence or the business cycle: What matters more for European expected returns?. Journal of Empirical Finance 28, 230-248. [Crossref]

148. Yi-Chun Ou, Lisette de Vries, Thorsten Wiesel, Peter C. Verhoef. 2014. The Role of Consumer Confidence in Creating Customer Loyalty. Journal of Service Research 17:3, 339-354. [Crossref]

149. Philipp an de Meulen, Martin Micheli, Torsten Schmidt. 2014. Forecasting real estate prices in Germany: the role of consumer confidence. Journal of Property Research 31:3, 244-263. [Crossref]

150. Antonis C. Simintiras, Volkan Yeniaras, Emrah Oney, Tajinder K. Bahia. 2014. Redefining Confidence for Consumer Behavior Research. Psychology & Marketing 31:6, 426-439. [Crossref]

151. Alexander Hogenboom, Bas Heerschop, Flavius Frasincar, Uzay Kaymak, Franciska de Jong. 2014. Multi-lingual support for lexicon-based sentiment analysis guided by semantics. Decision Support Systems 62, 43-53. [Crossref]

152. R. DAVID MCLEAN, MENGXIN ZHAO. 2014. The Business Cycle, Investor Sentiment, and Costly External Finance. The Journal of Finance 69:3, 1377-1409. [Crossref]

153. Dora Elizabeth Bock, Jacqueline Kilsheimer Eastman, Benjamin McKay. 2014. The impact of economic perceptions on status consumption: an exploratory study of the moderating role of education. Journal of Consumer Marketing 31:2, 111-117. [Crossref]

154. Lars Osberg, Andrew Sharpe. 2014. Measuring Economic Insecurity in Rich and Poor Nations. Review of Income and Wealth 60, S53-S76. [Crossref]

155. Nicholas Taylor. 2014. Economic forecast quality: information timeliness and data vintage effects. Empirical Economics 46:1, 145-174. [Crossref]

156. Giancarlo Bruno. 2014. Consumer confidence and consumption forecast: a non-parametric approach. Empirica 41:1, 37-52. [Crossref]

157. Mark Hutson, Fred Joutz, Herman Stekler. 2014. Interpreting and evaluating CESIfo's World Economic Survey directional forecasts. Economic Modelling 38, 6-11. [Crossref]

158. Pär Österholm. 2014. Survey data and short-term forecasts of Swedish GDP growth. Applied Economics Letters 21:2, 135-139. [Crossref]

159. Gabe J. de Bondt, Elke Hahn. 2014. Introducing the Euro Area-wide Leading Indicator (ALI): Real- Time Signals of Turning Points in the Growth Cycle from 2007 to 2011. Journal of Forecasting 33:1, 47-68. [Crossref]

160. Joshy Easaw, Atanu Ghoshray, Saeed Heravi. 2014. Households' Forming Subjective Expectations Using Perceived News: Do Shocks to ‘Good’ News Matter More Than ‘Bad’ News?. The Manchester School 82:1, 1-16. [Crossref]

161. Maria Isabel Marttnez Serna, Eliseo Navarro Arribas. 2014. Interest Rate Volatility and Expectations about the Business Cycle. SSRN Electronic Journal . [Crossref]

162. Dilyara Ibragimova. 2014. Consumer Expectations of Russian Populations: Cohort Analysis (1996-2009). SSRN Electronic Journal . [Crossref]

163. Jonathan Z. Berman, An T. K. Tran, John G. Lynch, Gal Zauberman. 2014. Expense Neglect Bias in Forecasting Personal Finances. SSRN Electronic Journal . [Crossref]

164. Viet Hoang Nguyen, Edda Claus. 2013. Good news, bad news, consumer sentiment and consumption behavior. Journal of Economic Psychology 39, 426-438. [Crossref]

165. Hunt Allcott. 2013. The Welfare Effects of Misperceived Product Costs: Data and Calibrations from the Automobile Market. American Economic Journal: Economic Policy 5:3, 30-66. [Abstract] [View PDF article] [PDF with links]

166. Stephane Dees, Pedro Soares Brinca. 2013. Consumer confidence as a predictor of consumption spending: Evidence for the United States and the Euro area. International Economics 134, 1-14. [Crossref]

167. Maurizio Bovi. 2013. Are the representative agent’s beliefs based on efficient econometric models?. Journal of Economic Dynamics and Control 37:3, 633-648. [Crossref]

168. Constantinos Antoniou, John A. Doukas, Avanidhar Subrahmanyam. 2013. Cognitive Dissonance, Sentiment, and Momentum. Journal of Financial and Quantitative Analysis 48:1, 245-275. [Crossref]

169. Christian Dreger, Konstantin Arkadievich Kholodilin. 2013. Forecasting Private Consumption by Consumer Surveys. Journal of Forecasting 32:1, 10-18. [Crossref]

170. Jing Jian Xiao. Family Economic Well-Being 573-611. [Crossref] 171. Juliane A. Lischka, Gabriele Siegert. 2013. Beeinflussen Wirtschaftsnachrichten auch

Wirtschaftserwartungen von Experten?. Studies in Communication Sciences 13:2, 174-184. [Crossref] 172. Luc Renneboog, Christophe Spaenjers. 2013. Buying Beauty: On Prices and Returns in the Art

Market. Management Science 59:1, 36-53. [Crossref] 173. Michael (Minye) Tang, Li Yao. 2013. Do Analysts Respond to Investor Sentiment? Evidence from the

Trade-Off between Quantity and Quality of Analyst Research. SSRN Electronic Journal . [Crossref] 174. Marta Lachowska. 2013. Expenditure, Confidence, and Uncertainty: Identifying Shocks to Consumer

Confidence Using Daily Data. SSRN Electronic Journal . [Crossref] 175. Paul Neisingh, Ad C.J. Stokman. 2013. What Drives Consumer Confidence in Times of Financial

Crises? Evidence for the Netherlands. SSRN Electronic Journal . [Crossref] 176. Yura Kim, Jaewoo Joo. 2012. Effect of Consumer Expectation Measured by Consumers on Advertising

Expenditures. Journal of the Korea Academia-Industrial cooperation Society 13:12, 5752-5758. [Crossref]

177. Stephan Jank. 2012. Mutual fund flows, expected returns, and the real economy. Journal of Banking & Finance 36:11, 3060-3070. [Crossref]

178. Shiu-Sheng Chen. 2012. Consumer confidence and stock returns over market fluctuations. Quantitative Finance 12:10, 1585-1597. [Crossref]

179. MARTHA A. STARR. 2012. CONSUMPTION, SENTIMENT, AND ECONOMIC NEWS. Economic Inquiry 50:4, 1097-1111. [Crossref]

180. Joscha Beckmann, Ansgar Belke, Michael Kühl. 2012. The cross-country importance of global sentiments—evidence for smaller EU countries. International Economics and Economic Policy 9:3-4, 245-264. [Crossref]

181. Seth A. Marvel, Hyunsuk Hong, Anna Papush, Steven H. Strogatz. 2012. Encouraging Moderation: Clues from a Simple Model of Ideological Conflict. Physical Review Letters 109:11. . [Crossref]

182. Manisha Singal. 2012. Effect of consumer sentiment on hospitality expenditures and stock returns. International Journal of Hospitality Management 31:2, 511-521. [Crossref]

183. Azizjon Alimov, Wayne Mikkelson. 2012. Does favorable investor sentiment lead to costly decisions to go public?. Journal of Corporate Finance 18:3, 519-540. [Crossref]

184. Dimitrios Gounopoulos, Dimitris Petmezas, Daniel Santamaria. 2012. Forecasting Tourist Arrivals in Greece and the Impact of Macroeconomic Shocks from the Countries of Tourists’ Origin. Annals of Tourism Research 39:2, 641-666. [Crossref]

185. Simon Shen. 2012. Future Comparative Competitiveness Under the Same Roof. Chinese Education & Society 45:2, 38-59. [Crossref]

186. Matthias Keese. 2012. Who feels constrained by high debt burdens? Subjective vs. objective measures of household debt. Journal of Economic Psychology 33:1, 125-141. [Crossref]

187. Luc Renneboog, Christophe Spaenjers. 2012. Buying Beauty: On Prices and Returns in the Art Market. SSRN Electronic Journal . [Crossref]

188. Maciej Kula. 2012. Using Textual Information in Econometrics: Quantifying Newspaper Sentiment. SSRN Electronic Journal . [Crossref]

189. Saurin Patel. 2012. Economic Optimism, Information Uncertainty and Future Investment Decisions: Evidence from the Mutual Fund Industry. SSRN Electronic Journal . [Crossref]

190. Yun Kim, Mark Setterfield, Yuan Mei. 2012. Aggregate Consumption and Debt Accumulation: An Empirical Examination of US Household Behavior. SSRN Electronic Journal . [Crossref]

191. Na Wang, Edward J. Zychowicz. 2012. Sentiment and Technical Analysis. SSRN Electronic Journal . [Crossref]

192. Karen Watkins, Juliana Vega. 2012. Crédito Al Consumo En Argentina Durante Períodos Normales Y De Crisis Económicas (Consumer Credit in Argentina During Normal and Crisis Periods). SSRN Electronic Journal . [Crossref]

193. Xia Wang, Yung-Hsin Chen. 2012. Is the Consumer Sentiment Index Capable of Forecasting the Transaction Activeness of Clients? Case Study for a Commercial Bank in China. Emerging Markets Finance and Trade 48:sup1, 20-28. [Crossref]

194. Cheng Li. 2011. Consumer expectation and output growth: The case of China. Economics Letters 113:3, 298-300. [Crossref]

195. Shumi Akhtar, Robert Faff, Barry Oliver. 2011. The asymmetric impact of consumer sentiment announcements on Australian foreign exchange rates. Australian Journal of Management 36:3, 387-403. [Crossref]

196. Alexander Hogenboom, Paul van Iterson, Bas Heerschop, Flavius Frasincar, Uzay Kaymak. Determining negation scope and strength in sentiment analysis 2589-2594. [Crossref]

197. Simeon Vosen, Torsten Schmidt. 2011. Forecasting private consumption: survey-based indicators vs. Google trends. Journal of Forecasting 30:6, 565-578. [Crossref]

198. Henk Berkman, Ben Jacobsen, John B. Lee. 2011. Time-varying rare disaster risk and stock returns. Journal of Financial Economics 101:2, 313-332. [Crossref]

199. Panagiotis Konstantinou, Athanasios Tagkalakis. 2011. Boosting confidence: Is there a role for fiscal policy?. Economic Modelling 28:4, 1629-1641. [Crossref]

200. JOSHY EASAW, ATANU GHOSHRAY. 2011. ‘THE ISLAND MAN'S BEHAVIOR’: SOME MICROFOUNDATIONS OF HOW HOUSEHOLDS FORM MACROECONOMIC EXPECTATIONS*. The Manchester School 79:4, 789-810. [Crossref]

201. Paul De Grauwe. 2011. Animal spirits and monetary policy. Economic Theory 47:2-3, 423-457. [Crossref]

202. David Hollanders, Rens Vliegenthart. 2011. The influence of negative newspaper coverage on consumer confidence: The Dutch case. Journal of Economic Psychology 32:3, 367-373. [Crossref]

203. Shumi Akhtar, Robert Faff, Barry Oliver, Avanidhar Subrahmanyam. 2011. The power of bad: The negativity bias in Australian consumer sentiment announcements on stock returns. Journal of Banking & Finance 35:5, 1239-1249. [Crossref]

204. Shiu-Sheng Chen. 2011. Lack of consumer confidence and stock returns. Journal of Empirical Finance 18:2, 225-236. [Crossref]

205. R. David McLean, Mengxin Zhao. 2011. The Business Cycle, Investor Sentiment, and Costly External Finance. SSRN Electronic Journal . [Crossref]

206. Richard Friberg, Cristian Huse. 2011. Using Demand Systems to Evaluate Risky Projects: An Application to the Automobile Industry. SSRN Electronic Journal . [Crossref]

207. Gabe De Bondt, Stefano Schiaffi. 2011. Confidence Matters for Nowcasting GDP: Euro Area and U.S. Evidence from a PMI-Based Model. SSRN Electronic Journal . [Crossref]

208. Ruben Cox. 2011. Behavioral Aspects of Insurance Purchase: Evidence from Mortgage Insurance. SSRN Electronic Journal . [Crossref]

209. Saurin Patel. 2011. Economic Optimism, Information Uncertainty and Future Investment Decisions: Evidence from the Mutual Fund Industry. SSRN Electronic Journal . [Crossref]

210. Joshy Easaw. 2010. It’s all ‘bad’ news! Voters’ perception of macroeconomic policy competence. Public Choice 145:1-2, 253-264. [Crossref]

211. Oscar Claveria, Jordi Datzira. 2010. Forecasting tourism demand using consumer expectations. Tourism Review 65:1, 18-36. [Crossref]

212. Ashi Küçükaslan, Sadullah Çelik. 2010. WOMEN FEEL MORE PESSIMISTIC THAN MEN: EMPIRICAL EVIDENCE FROM TURKISH CONSUMER CONFIDENCE INDEX. Journal of Business Economics and Management 11:1, 146-171. [Crossref]

213. Joshy Easaw, Atanu Ghoshray. 2010. News and households’ subjective macroeconomic expectations. Journal of Macroeconomics 32:1, 469-475. [Crossref]

214. Claes Fornell, Roland T. Rust, Marnik G. Dekimpe. 2010. The Effect of Customer Satisfaction on Consumer Spending Growth. Journal of Marketing Research 47:1, 28-35. [Crossref]

215. Alexander Hogenboom, Frederik Hogenboom, Uzay Kaymak, Paul Wouters, Franciska de Jong. Mining Economic Sentiment Using Argumentation Structures 200-209. [Crossref]

216. Jeff Dominitz, Arthur van Soest. Survey Data, Analysis of 314-322. [Crossref] 217. John A. Doukas, Constantinos Antoniou, Avanidhar Subrahmanyam. 2010. Sentiment and

Momentum. SSRN Electronic Journal . [Crossref] 218. Henk Berkman, Ben Jacobsen, John B. Lee. 2010. Time-Varying Rare Disaster Risk and Stock

Returns. SSRN Electronic Journal . [Crossref] 219. Shumi M. Akhtar, Robert W. Faff, Barry R. Oliver, Avanidhar Subrahmanyam. 2010. The Power of

Bad: The Negativity Bias in Consumer Sentiment Announcements on Stock Returns. SSRN Electronic Journal . [Crossref]

220. Christian Dreger, Konstantin A. Kholodilin. 2010. Forecasting Private Consumption by Consumer Surveys. SSRN Electronic Journal . [Crossref]

221. Torsten Schmidt, Simeon Vosen. 2010. A Monthly Consumption Indicator for Germany Based on Internet Search Query Data. SSRN Electronic Journal . [Crossref]

222. Yasemin Özerkek, Sadullah Çelik. 2010. The link between government spending, consumer confidence and consumption expenditures in emerging market countries. Panoeconomicus 57:4, 471-485. [Crossref]

223. Thomas Lux. 2009. Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey. Journal of Economic Behavior & Organization 72:2, 638-655. [Crossref]

224. Maurizio Bovi. 2009. Economic versus psychological forecasting. Evidence from consumer confidence surveys. Journal of Economic Psychology 30:4, 563-574. [Crossref]

225. Martin Cherkes, Jacob Sagi, Richard Stanton. 2009. A Liquidity-Based Theory of Closed-End Funds. Review of Financial Studies 22:1, 257-297. [Crossref]

226. ALI AL-EYD, RAY BARRELL, E. PHILIP DAVIS. 2009. CONSUMER CONFIDENCE INDICES AND SHORT-TERM FORECASTING OF CONSUMPTION*. The Manchester School 77:1, 96-111. [Crossref]

227. David Hollanders, Rens Vliegenthart. 2009. The Influence of Negative Newspaper Coverage on Consumer Confidence: The Dutch Case. SSRN Electronic Journal . [Crossref]

228. Darren P. Grant. 2009. What Makes a Good Economy? An Analysis of Survey Data. SSRN Electronic Journal . [Crossref]

229. Torsten Schmidt, Simeon Vosen. 2009. Forecasting Private Consumption: Survey-Based Indicators vs. Google Trends. SSRN Electronic Journal . [Crossref]

230. Ansgar Hubertus Belke, Joscha Beckmann, Michael Kühl. 2009. Global Integration of Central and Eastern European Financial Markets: The Role of Economic Sentiments. SSRN Electronic Journal . [Crossref]

231. Sadullah Çelik, Yasemin Özerkek. 2009. Panel cointegration analysis of consumer confidence and personal consumption in the European Union. Journal of Business Economics and Management 10:2, 161-168. [Crossref]

232. Joshy Easaw, Atanu Ghoshray. 2008. The cyclical nature of Consumer Sentiments Indices in the US and UK. The Journal of Socio-Economics 37:5, 1994-1998. [Crossref]

233. Douglas J. Lamdin. 2008. Does Consumer Sentiment Foretell Revolving Credit Use?. Journal of Family and Economic Issues 29:2, 279-288. [Crossref]

234. Rutger van Oest, Philip Hans Franses. 2008. Measuring changes in consumer confidence. Journal of Economic Psychology 29:3, 255-275. [Crossref]

235. Karel Jan Alsem, Steven Brakman, Lex Hoogduin, Gerard Kuper. 2008. The impact of newspapers on consumer confidence: does spin bias exist?. Applied Economics 40:5, 531-539. [Crossref]

236. JAN ROUWENDAL, SIMONETTA LONGHI. 2008. The Effect of Consumers' Expectations in a Booming Housing Market: Space-time Patterns in the Netherlands, 1999–2000. Housing Studies 23:2, 291-317. [Crossref]

237. Eva Ferreira, M. Isabel Martínez Serna, Eliseo Navarro, Gonzalo Rubio. 2008. Economic Sentiment and Yield Spreads in Europe. European Financial Management 14:2, 206-221. [Crossref]

238. Jeff Dominitz, Arthur van Soest. Survey Data, Analysis of 1-7. [Crossref] 239. Joshy Z. Easaw, Atanu Ghoshray. 2007. Confidence or competence: Do presidencies matter for

households’ subjective preferences?. European Journal of Political Economy 23:4, 1025-1037. [Crossref] 240. Martin Sommer. 2007. Habit Formation and Aggregate Consumption Dynamics. The B.E. Journal

of Macroeconomics 7:1. . [Crossref]

241. Sarah Gelper, Aurélie Lemmens, Christophe Croux. 2007. Consumer sentiment and consumer spending: decomposing the Granger causal relationship in the time domain. Applied Economics 39:1, 1-11. [Crossref]

242. Jan Rouwendal, Simonetta Longhi. 2007. The Effect of Consumers' Expectations in a Booming Housing Market: Space-Time Patterns in the Netherlands, 1999-2000. SSRN Electronic Journal . [Crossref]

243. Ray Barrell, E. Philip Davis, Olga Pomerantz. 2006. Costs of financial instability, household-sector balance sheets and consumption. Journal of Financial Stability 2:2, 194-216. [Crossref]

244. LUCIA F. DUNN, IDA A. MIRZAIE. 2006. TURNS IN CONSUMER CONFIDENCE: AN INFORMATION ADVANTAGE LINKED TO MANUFACTURING. Economic Inquiry 44:2, 343-351. [Crossref]

245. Michael Lemmon, Evgenia Portniaguina. 2006. Consumer Confidence and Asset Prices: Some Empirical Evidence. Review of Financial Studies 19:4, 1499-1529. [Crossref]

246. Lily Xiaoli Qiu, Ivo Welch. 2006. Investor Sentiment Measures. SSRN Electronic Journal . [Crossref] 247. Maurizio Bovi. 2006. Consumers Sentiment and Cognitive Macroeconometrics Paradoxes and

Explanations. SSRN Electronic Journal . [Crossref] 248. Michael W. M. Roos. 2005. TV Weather Forecast or Look through the Window? Expert and

Consumer Expectations about Macroeconomic Conditions. Kyklos 58:3, 415-437. [Crossref] 249. Burçin Güçlü, Miguel-Ángel Canela. Analytics Overuse in Advertising and Promotion Budget

Forecasting 163-191. [Crossref] 250. Burçin Güçlü, Miguel-Ángel Canela. Analytics Overuse in Advertising and Promotion Budget

Forecasting 477-505. [Crossref]