Term Paper Description
Your term paper assignment is replicate a least squares regression (OLS or feasible GLS) contained in a published economics paper and to re-estimate the model using a new data set. Unless you receive explicit permission from me, the date for your term paper regression analysis must use a cross-sectional data set, not a time series, pooled, or panel dataset.
The following is an outline for the write-up of your paper. You are encouraged to adjust this outline in ways that are relevant to your paper. Although there is no minimum length for the term paper, a paper with a body under 8 double-spaced pages with 1-inch margins (excluding title page, abstract, graphs, tables, bibliography, and appendices) seldom covers the material adequately. The maximum length for the paper (again, excluding …) is 15 pages.
1. Title page.
2. Abstract. Summarize your paper in one paragraph (single spaced). This may be placed on a separate page or prior to, but on the same page as, the beginning of your introduction.
3. Introduction. Discuss, in general terms, the subject and purpose of your paper.
4. Literature review. This is an overview of journal article containing the regression you plan to replicate and the roll of the regression in the paper.
6. Data. Define and describe the data used in your regressions. Report and discuss summary statistics of your data.
7. Regression results. Report and discuss your regression results. Discuss the success of your replication and whether you obtain the same basic results with the new data set.
8. Conclusion. Summarize the major results.
9. Bibliography. Complete citations for all articles and books you have used preparing your paper including, but not limited to, the article discussed in your literature review.
10. Data appendix. This consists of the source(s) of your data and any data manipulation used to create the variables and observations used in your regressions.
In addition to your term paper you must submit a file containing your datasets and the R Markdown file containing your data analysis.
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