Need one who is good at Economic data analysis and using Stata(A Software) (Undergraduate)

Second deliverable for final project – due Thursday 11/30 5:00 p.m.

Along with other students who have chosen the same primary topic, create a single table of regression analysis and discuss the results in the text of your paper. Be sure to correct any problems that were mentioned in the review of your first draft. Your grade on this deliverable will be based on the content of your analysis, but also whether you are able to generate a document that is professional in its appearance and content. Your intended audience is someone who would have the knowledge that is expected of someone who has mastered the content in Economics 311.

1. Provide at least 2 tests of alternative specifications (e.g. log vs linear, linear vs quadratic, dummy variables vs continuous, etc.) In the text, describe the specifications you compared and the preferred specification based on your analysis. Present the results of your regression analysis and the relevant test-statistics in a professional table. Be sure to discuss the results of your analysis in the text.

2. For each specification considered in part (1), provide a Breusch-Pagan test and the simple version of the White test (2nd form discussed in notes) for heteroscedasticity. Include the test statistic and corresponding p-values for these test statistics in your regression table. In the text of your deliverable, describe the basis for the conclusions you draw from your heteroscedasticity tests. If you find heteroscedasticity, are there specific characteristics that cause the variance of the residual to be higher or lower? Explain how you came to this conclusion.

3. If there is evidence of heteroscedasticity, add another column displaying your preferred regression from (1) but using robust standard errors. If you are estimating a linear probability model, use weighted least squares if possible – but be sure to investigate whether WLS would result in negative weights. If WLS results in negative weights, discuss how you determined this.

4. Discuss whether your expected effects for your key control variable and at least two others that you included in your first deliverable are confirmed by the preferred specification you identified above. Discuss whether these effects are statistically significant at the .05 level.

5. Discuss the “economic significance” of the effects for your two control variables. For example, describe the effect of a one standard deviation change in continuous control variables on the dependent variable; or a switch from 0 to 1 for a dummy variable.

6. In order that your table be deemed professional, review the document posted on my website. The regression table should be self-explanatory. The reader should be able to determine what kind of regression was estimated, how the sample was created, and what all the variables measure without referring to the text. Examples of appropriate tables are provided in the document “Creating effective tables” that is posted in the Canvas project module. Specific elements of the table that should make the table self-explanatory are as follows:

a. Title (make it clear what the table is about – e.g. Determinants of Household Electricity Expenditures in 2016).

b. Column and row headings (See sample tables for examples of relevant column headers).

c. Notes attached to the table that explain i. The source of data for the table, including relevant sample restrictions (e.g.

households aged 25-55 who have a mortgage). ii. Whether the table has t-statistics or standard errors in parentheses and the

type of regression (e.g. OLS, linear probability model estimated with OLS, etc). If robust standard errors are used for calculation of standard errors or t- statistics, make that clear. (e.g. t-statistics are in parentheses. Robust standard errors are used in specifications 3 and 4.)

iii. Anything that needs to be clarified about variables or methods can be stated in the list of variables or footnotes to the table. (e.g. income is measured in 1000s of 2016 dollars)

iv. See Miller for examples of how to use notes in tables. Notice that notes in tables are referenced with a letter (not a number).

d. Variable names that are easily understood. Some examples: i. years of education, not educ

ii. Number of children, not NCHILd iii. Household Income in 2016 dollars, not income iv. Be sure units of measurement are clear (e.g. 1000s of dollars, birthweight in

pounds). v. If you are using dummy variables for categories, group them together and make

it clear which dummy was omitted). e. Make it clear what the dependent variable is and also indicate sample size and either R2

or adjusted R2 (or both). f. Regression tables may also present only a subset of coefficients and mention in a note

or row whether other variables were included in the regression. See the sample tables provided on the projects webpage. Miller (table 4.2) provides guidance on the appropriate number of digits. If coefficients are “too small”, rescale the relevant control variable to adjust (e.g. measure income in 1000s of dollars instead of dollars).

g. Regression tables should start at the top of the page, and if they must span across pages, be sure to “break” them at a reasonable point (e.g. don’t split a row with coefficients on one table and t-statistics on the next).

The sections of your paper should include the following.

1. Title page (title, authors, date) 2. Introduction: Summarize what was learned in first deliverable and what is added in this

deliverable. 3. Discussion of results from new regression analysis (Table 1).

a. Different specifications considered and the test statistics you used to decide between the specifications.

b. The implications of the heteroskedasticity tests for each specification (i.e. do you find evidence of heteroskedasticity) and how this affected your decision to use robust standard errors in your preferred specification.

4. Discussion of whether the key control variable and at least two others fit your prior expectations. Be sure to explain why you expected the effect of the control variable on the dependent variable to be either positive or negative.