Econn-09

ECO 620 MILESTONE TWO

“Do economic events affect the outcome of elections in U.S?”

ECO 620 MILESTONE TWO 5

“Do economic events affect the outcome of elections in U.S?”

ECO 620 MILESTONE TWO

Data

The data to be used in this study will be obtained from the secondary sources. Secondary data is readily available from various sources (Creswell & Plano Clark, 2007). However, secondary data may impose some limitations on the choice of empirical method. First, secondary data is mainly collected for a general purpose and thus inappropriate for a specific study (Özerdem & Bowd, 2010). This means that secondary data may not answer our specific research question. For example, if we intend to conduct a study by relying on secondary data collected over the past forty years and the only relevant data available is for the past thirty years then this means that we our results of the study will be compromised. Second, secondary data is subject to errors or misinterpretation based on data collection methods that were used (Özerdem & Bowd, 2010). The errors in secondary data will have a negative impact on the results of the study Comment by Author: Great point.

Finally, secondary data collected may be lacking the required quality and sometimes it is expressed in an inappropriate form (Creswell & Plano Clark, 2007). Quality data refers to the data that is reliable and relevant. In essence, secondary data should be recent and expressed in a form that can be easily utilized by the researcher (Creswell & Plano Clark, 2007). Absence of quality secondary data may lead to poor estimation of the regression model. In general, weaknesses of secondary data will have a negative impact on the overall regression results. To address this issue, I intend to use the most reliable secondary sources of information. For example, the US Central Bureau website provides the most recent and reliable data on various variables. Comment by Author: Yes, and reliable also includes having the right definition of the variable.

Empirical Approach

I intend to use the ordinary least squares (OLS) estimation technique to estimate my empirical model. The ordinary least square (OLS) regression shows the relationship between the dependent variable and explanatory variables (Baltagi, 2011). In this case, the assumption made when using the OLS technique is that the dependent variable is a linear function of explanatory variables (Baltagi, 2011). Therefore it would be appropriate to use this method since it would enable us to establish the linear relationship between interest rate change, unemployment rate change and percentage of votes garnered by the incumbent.

However, OLS estimation technique has got some disadvantages. First, presence of outliers, i.e., extremely high or small values for the dependent variable in comparison to the values of explanatory variables may lead to poor estimation of the regression model (Baltagi, 2011). Second, sometimes the explanatory variables are correlated and this may lead to a biasness and inconsistency of estimated coefficients (Baltagi, 2011). The estimated coefficients may have wrong signs which do not make economic sense. Finally, OLS estimation technique assumes that dependent and explanatory variables are always linearly related (Baltagi, 2011). This is not always true in reality since most systems are non-linear. Other possible alternative methods that can be used include the logit and probit models (Baltagi, 2011). These models are preferred over OLS in certain circumstances. For example, the logit model allows the properties of a linear regression model to be exploited. The model specification is as follows:

-The independent variable (Y) is the percentage of votes garnered by the incumbent.

- The explanatory variables are interest rate change (X2) expressed as a percentage and unemployment rate change (X3) also expressed as a percentage.

X2 = [(interest rate in an election year- interest rate in the preceding year)/ interest rate in the preceding year] *100%

X3 = [(Unemployment rate in an election year- Unemployment rate in the preceding year)/ Unemployment rate in the preceding year] *100%

These variables are expressed in the equation shown below:

Yt = B1+B2X2t +B3X3t + ut

Percentage of votes garnered by the incumbent is taken to be the dependent variable since it is influenced by the other two variables.

Results and Robustness Check Comment by Author: Can you provide the regression output as well!

The preliminary results show that the coefficients of X2 and X3 are negative. Our original hypothesis attempted to establish whether interest rate change and unemployment rate influences political outcomes. Indeed the preliminary results show that indeed political outcomes are negatively affected by these two variables. I anticipated the correlation between the two explanatory variables. Usually, OLS estimation technique assumes that there is absence of multicollinearity between the explanatory variables. To test for the presence and severity of multicollinearity, we compute the variance inflation factor (VIF) of various explanatory variables Comment by Author: Are the results significant?

(Baltagi, 2011). If the variance inflation factor lies between 1 and 10 then there is no multicollinearity. If the variance inflation factor is less than 1 or greater than there is multicollinearity (Baltagi, 2011).

To reduce multicollinearity, we eliminate variables with high VIF values and estimating the regression model again. The secondary results of the regression should indicate absence of multicollinearity. In this case the VIF values of the remaining variables will fall between 1 and 10 and the estimated coefficients will have the correct signs.

Note: In future submissions please submit the regression results.

References

Baltagi, B. (2011). Econometrics. Berlin: Springer.

Creswell, J., & Plano Clark, V. (2007). Designing and conducting mixed methods research.

Thousand Oaks, Calif.: SAGE Publications.

Özerdem, A., & Bowd, R. (2010). Participatory research methodologies. Farnham,

England: Ashgate.