Please submit one-page Proposal before 6, Nov and a 6 page report before 1, December.
You will be working on a project where you use multivariate regression analysis to analyze
economic data. You will be responsible for determining the research question, formulating the
regression model, finding the relevant data and papers, performing the analysis and discussing the
results. Chapter 19 in Wooldridge’s “Introductory Econometrics” has many useful examples and
suggestions for carrying out an empirical project.
Proposal (one-page):
What is the research project for this course?
Consider a research question that can be answered via application of regression analysis to economic
data. Choose a variable of interest (a dependent variable) and some explanatory variables; then identify
and quantify the effects of these independent variables on the dependent variable.
Limitation: use only cross-sectional data; the dependent variable must be continuous.
How to come up with a research project topic?
Please read Chapter 19 in Wooldridge: “Carrying out a research project”.
Find a research topic based on your personal interests, questions discussed in the economics classes and
on data availability.
Requirment:
one paragraph with the description of the research question and brief motivation;
3. a list of dependent and independent variables and a brief discussion of the effect you are
planning to measure;
4. description of the data set you will be using, including the source of the data (provide a link to
the corresponding website if applicable), the number of observations, and names of the relevant
variables.
5. references to two published papers that analyze a similar question.
Report (6 page):
1. Introduction
In this section, describe the research question and explain why it is important. Focus on the
dependent variable. Provide a brief description of what you will do in your project (in each section),
without getting into detail.
2. Literature
review
Provide a short review of journal articles and/or books that are closely related to your project.
Include the complete reference for each reviewed study in the reference section.
3. Methodology
This section must discuss in detail what you will do in this project. You should mention the
questions that you will answer and how you plan to do so. For example, you write that you will
investigate the effect of education and experience on wages. This will be done by considering a
multivariate linear model, to be estimated by OLS. If there is a similar paper in the literature, you
must explain the difference between your work and the cited paper. Is it in the methodology? Do
you include more independent variables in your analysis? Do you use a different estimation
technique? Do you have a different data set?
Remember to write the regression that you plan to run using the following format:
= + +
Focus on the independent variables. For each independent variable, explain why you have included
it in the model and whether you expect it to have a positive or negative impact on the dependent
variable.
4. Description of the data
In this section, you describe your data set in detail: the variables, their nature (continuous,
categorical, or binary 0/1), time period that they span, the number of observations, and the source
of the data.
Summary statistics should be provided either in tables or figures, depending on the type of data.
The full range of summary statistics (mean/variance/min/max/skewness/kurtosis) can be provided
for continuous variables. Binary or categorical variables can be reported using frequency tables or
pie charts.
Provide some discussion of the descriptive statistics of the dependent and independent variables.
If you notice some patterns in your data, interesting or strange, mention them here. You can also
include some preliminary analysis about the relationship between variables of interest using
scatterplots between pairs of variables.
5. Results
In section 3 you have explained your methodology. In this section, you should estimate the models
based on your data and report the results. The regression outputs and specification tests must be
provided and discussed. In the class you will learn how to estimate the models and how to do
inference for the models (i.e., testing hypotheses about the values of the parameters of your model
based on OLS estimates). You are asked to use what you have learned to estimate your models
and make inference. In this section, you will also discuss the model specification and potential
biases. You may consider additional independent variables that matter for explaining the
dependent variable or use different nonlinear transformations of existing variables.
If you have regressed the same variable of interest on different independent variables, you should
discuss which resulting model is better in terms of the goodness of fit (
2
and
2
).
You need to be aware that your results would be reliable if OLS assumptions are satisfied. After
running the regressions, you should test for functional misspecification and heteroskedasticity.
Note: Detailed instructions for this section, as well as relevant STATA commands, will be posted
later on Brightspace
6. Conclusion
This is the final section of your project. You should provide a summary of what you have done. In
one or at most two paragraphs, state the questions that you wished to answer and your main
findings (independent variables that have some effects on the dependent variables and magnitude
of each effect). How can you use these results for policy making (practical purposes)? You may
also provide suggestions for further research (i.e., including other variables, considering different
functional forms, using different estimators, etc.).
7. References
You should list all cited studies that are related to your research questions following the Chicago
Manual of Style as follows:
Andrews D., and E. Zivot, (1992), Further Evidence on the Great Crash, the Oil-Price, and the
Unit-Root Hypothesis, Journal of Business & Economic Statistics, 10, 251-270.
Appendix
Including tables and figures in the main text may lead to some difficulties regarding the layout of
your work. Instead, you may place all your tables and figures at the end of the file. All tables and
figures should be labeled (e.g. Table 1, Figure 5, etc.) and must have a title. When you discuss the
results in the text, use table and figure numbers to refer to them. In Section 5, refer to relevant
tables and figures when discussing the results as follows:
“The results of running the regression. ……. can be found in Table 2. The parameter estimate for
education is statistically insignificant….”
If you move all tables and figures to the Appendix, the Appendix should have two separate
sections: one for tables and one for figures. Tables and figures should not be copy-pasted from the
software output. You should create your own tables and graphs.
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