empirical analysis

Semester Project

The purpose of this project is for you to gain experience in applying methods taught in this class to a real data set of interest to you. The project will simulate the real world practices of defining research questions, identifying variables, producing data, analyzing data, writing a report, and evaluating peers. Each of these skills is critical to any career path you may choose.

For the final project, you should work alone. The purpose of the project is for you to gain experience in applying the methods taught in the class to a real data set of interest to you.

Objectives

At the end of this project, you will be able to:

1. define a research question and define appropriate variables to measure on a topic that you care about

2. decide on an experimental design

3. gather and analyze original data (rather than data prepared for you) on an issue of interest to you

4. prepare an appropriate report

5. provide constructive, thoughtful feedback to peers on their projects

Project Suggestions

Conducting an empirical analysis of economic data can be rewarding and informative. The first step in conducting an empirical analysis is choosing the topic you want to study and, within that topic, the specific question or questions you will investigate. Although there is not a single best way to choose a topic, the following suggestions might be useful.

1. Pick a topic that you find personally interesting, ideally one about which you already have some knowledge. The topic might be related to your career interests, summer work you did, employment experience of a family member, or something of intellectual interest to you. Often, a specific policy problem, a personal decision, or a business issue raises questions that can be addressed by an empirical study.

2. Make the question that will be the main focus of your study as specific as possible. The more narrowly the question relates to a measurable causal effect, the easier it will be to answer.

3. Check the related literature. You might find published studies on topics closely related to yours. Use previous work to give you ideas about data sources and about what questions have not yet been answered.

4. Choose a question that can be answered using the available data. Although the question you originally pose might not be answerable using available data, the data might support the analysis of a related and equally interesting question.

5. Share your topic on the discussion board. If you find your topic interesting, then the odds are that others will too, and an instructor or classmate might suggest an angle that you have not thought of.

As shown in the table below, the course project consists of 6 checkpoints that lead up to, and include, the final report.

Activity	Deadline*	Submission
Project Checkpoint 1: Topic Selection	Monday, February 8 by 3am	Moodle Discussion board
Project Checkpoint 2: Hypothesis & Research Question	Monday, February 22 by 3am	Moodle Discussion board
Project Checkpoint 3: Identify Variables for Study	Monday, March 14 by 3am	Moodle Discussion Board
Project Checkpoint 4: Data Sets	Monday, April 4	NA
Project Checkpoint 5: Regression Analysis	Monday, April 25	NA
Project Checkpoint 6: Final Report	Monday, May 2 by 3am	Submit to be peer reviewed
Peer Evaluation for Project Checkpoint 6	Thursday, May 5 at 3am	Submit your evaluations of your peers' projects

* Unless otherwise noted, all deadlines are Central Time (time zone conversion)

Project Format

This project relies on multiple regression analysis to analyze a data set that is of interest to you. The final report for the project should be a 5-10 page single-spaced paper that describes the questions of interest, how you used your data set to analyze these questions with details on the steps you used in your analysis, your findings about your question of interest and the limitations of your study. Specifically, your report should contain the following:

1. Introduction. The introduction succinctly states the problem you are interested in, briefly describes your data and the method of analysis, and summarizes your main conclusions. A summary of what you set out to learn, and what you ended up finding. It should summarize the entire report.

2. Data Description. This section provides the details of the data sources, any transformations you have done to the data (for example, changing the units of some variables), gives a table of summary statistics (means and standard deviations) of the variables, and provides scatterplots and/or other relevant plots of the data. If there are outliers other than those arising from corrected typographical or computer errors, this is the place to point them out.

3. Regression Analysis. Describe how you used multiple regression to analyze the data set. Specifically, you should discuss how you carried out the steps in analysis discussed in class, i.e., exploration of data to find an initial reasonable model, checking the model and changes to the model based on your checking of the model.

4. Empirical Results. This section provides the main empirical results in the paper. Conventionally, regression results are presented in tabular form, with footnotes clearly explaining the entries. The initial table of results should present the main results; sensitivity analysis using alternative specifications can be presented in additional columns in that table or in subsequent tables. For organizational purposes and clarity, you may chose to have some tables at the end of the paper, with appropriate references in the body of the paper as needed. The text should provide a careful discussion of the results, including assessments both of statistical significance and of economic significance, that is, the magnitude of the estimated relations in a real-world sense.

5. Summary and Discussion. This section summarizes your main empirical findings and discusses their implications for the original question of interest. Describe any limitations of your study and how they might be overcome in future research and provide brief conclusions about the results of your study.

Criteria	Grade Levels
Introduction	Absent: the criteria is completely absent.	Poor: The introductions does not state the problem of interest or describe the data and method of analysis. It does not summarize main conclusions, and does not provide a summary of what we set out to learn. There is essentially no introduction.	Fair: There is a short introduction that may or may not state the problem of interest. It mentions the data, but does not describe it. No major conclusions are drawn.	Good: The introduction is a bit lengthy or short but does state the problem of interest, describes data and methods of analysis, although not clearly. Main conclusions are presented are not summarized.	Excellent: The introduction succinctly states the problem of interest, briefly describes the data and method of analysis, and summarizes main conclusions, a summary of what you set our to learn, and what you ended up finding.
Data Description	Absent: the criteria is completely absent.	Poor: This section does not detail data sources, transformations, nor does it give any summary statistics. It does not provide any graphically relevant information.	Fair: This section provides very little information in regards to data sources, transformations, and summary statistics. There are few or no scatter plots or other relevant graphical representations.	Good: This section covers data sources, transformations, summary statistics, and scatter plots, and outliers if there are any. However, the order, structure, and presentation of the data does not flow well.	Excellent: Provides details of data sources, transformations of the data, gives a table of summary statistics, and provides scatter plots, and other relevant plots of the data. If there are outliers, they are made obvious.
Regression Analysis	Absent: the criteria is completely absent.	Poor: The use of multiple regression is not clear nor is the regression model explained. There are no references made to class material.	Fair: A multiple regression model is evident but is not clearly explained. Few, if any, references to the course material is made.	Good: The use of multiple regression is made clear, but there is not strong evidence supporting the use of a particular model. There are references made to supporting course material to justify decisions.	Excellent: How multiple regression was used is described. The steps of analysis that were covered in readings/lectures is discussed. The process for deciding on a particular regression model is well discussed and justified.
Empirical Results	Absent: the criteria is completely absent.	Poor: This section has no appropriate empirical results presented. There may be regression results that do not refer to the regression model that was outlined in the regression analysis section. There is no discussion surrounding the results.	Fair: Multiple regression results are presented. However, there is little to no discussion of the results, and if subsequent tables are needed, they are not presented.	Good: All results of analysis are presented. However, the discussion of the results is unclear or inaccurate.	Excellent: Regression results are presented in tabular form with footnotes explaining entries. The table presents main results while sensitivity analysis or alternative specifications are outlined in subsequent tables. The text provides a careful discussion of the results, including assessments of both statistical and economical significance.
Summary and Discussion	Absent: the criteria is completely absent.	Poor: There is no summary or discussion of the empirical results. No limitations for the study are presented, and there is no conclusion.	Fair: There is little understanding or description of the empirical results and their implications for the original question of interest. There are little to no limitations presented and an unclear conclusion.	Good: Empirical findings are summarized and implications refer to the original question are limited. Limitations of the study are very briefly addressed, and the conclusion of the study could be clearer.	Excellent: This section summarizes the main empirical findings and discusses implications. Limitations of the study are addressed so that they may be overcome in the future. A very brief conclusion of the results is presented.