R studio required. 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.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):Consider a research question that can be answered via application of regression analysis to economicdata. Choose a variable of interest (a dependent variable) and some explanatory variables; then identifyand 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 andon 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. IntroductionIn 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. Literaturereview 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. MethodologyThis 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: 𝑤𝑎𝑔𝑒𝑖 = 𝛼 + 𝛽𝑒𝑑𝑢𝑖 + 𝑢𝑖 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 dataIn 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. ResultsIn 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 (𝑅 2 and 𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅 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 Brightspace6. ConclusionThis 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. ReferencesYou 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.AppendixIncluding 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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