Econ3338.01: Introduction to Econometrics IYou will be working on a project where you use multivariate regression analysis to analyzeeconomic data. You will be responsible for determining the research question, formulating theregression model, finding the relevant data and papers, performing the analysis and discussing theresults. Chapter 19 in Wooldridge’s“Introductory Econometrics”has many useful examples andsuggestions for carrying out an empirical project.Technical Details-FormatsYour final project must be submitted uploaded to a designated folder on Brightspace by December15, 2020.Please submit an electronic copy of your first draft for a format check by December 6, 2020. Ifany important part of the paper is missing or is not properly presented, you will receive an emailwithin 3-4 days.1. Cover page. The cover page should be structured as follows:NameB00#DateProject TitlePrepared for ECON 3338.01: Introduction to Econometrics2. Length. The maximum length, including figures, tables and references, should not exceed12 pages.3. Font size and space. The text should be double-spaced, with size 12 font.4. Equations. Use an equation editor (built-in in MS Word) to specify your model(s), andnumber all equations in your text sequentially (1, 2, etc).Proposed Outline1. IntroductionIn this section, describe the research question and explain why it is important. Focus on thedependent variable. Provide a brief description of what you will do in your project (in each section),without getting into detail.2. Literature reviewProvide 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 thequestions that you will answer and how you plan to do so. For example, you write that you willinvestigate the effect of education and experience on wages. This will be done by considering amultivariate linear model, to be estimated by OLS. If there is a similar paper in the literature, youmust explain the difference between your work and the cited paper. Is it in the methodology? Doyou include more independent variables in your analysis? Do you use a different estimationtechnique? Do you have a different data set?Remember to write the regression that you plan to run using the following format:𝑤𝑎𝑔𝑒𝑖= 𝛼 + 𝛽𝑒𝑑𝑢𝑖 + 𝑢𝑖Focus on the independent variables. For each independent variable, explain why you have includedit in the model and whether you expect it to have a positive or negative impact on the dependentvariable.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 sourceof 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 providedfor continuous variables. Binary or categorical variables can be reported using frequency tables orpie 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 alsoinclude some preliminary analysis about the relationship between variables of interest usingscatterplots between pairs of variables.5. ResultsIn section 3 you have explained your methodology. In this section, you should estimate the modelsbased on your data and report the results. The regression outputs and specification tests must beprovided and discussed. In the class you will learn how to estimate the models and how to doinference for the models (i.e., testing hypotheses about the values of the parameters of your modelbased 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 potentialbiases. You may consider additional independent variables that matter for explaining thedependent variable or use different nonlinear transformations of existing variables.If you have regressed the same variable of interest on different independent variables, you shoulddiscuss which resulting model is better in terms of the goodness of fit (𝑅2and 𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅2).You need to be aware that your results would be reliable if OLS assumptions are satisfied. Afterrunning the regressions, you should test for functional misspecification and heteroskedasticity.Note: Detailed instructions for this section, as well as relevant STATA commands, will be postedlater on Brightspace6. ConclusionThis is the final section of your project. You should provide a summary of what you have done. Inone or at most two paragraphs, state the questions that you wished to answer and your mainfindings (independent variables that have some effects on the dependent variables and magnitudeof each effect). How can you use these results for policy making (practical purposes)? You mayalso provide suggestions for further research (i.e., including other variables, considering differentfunctional forms, using different estimators, etc.).7. ReferencesYou should list all cited studies that are related to your research questions following the ChicagoManual of Style as follows:Andrews D., and E. Zivot, (1992), Further Evidence on the Great Crash, the Oil-Price, and theUnit-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 ofyour work. Instead, you may place all your tables and figures at the end of the file. All tables andfigures should be labeled (e.g. Table 1, Figure 5, etc.) and must have a title. When you discuss theresults in the text, use table and figure numbers to refer to them. In Section 5, refer to relevanttables and figures when discussing the results as follows:“The results of running the regression. ……. can be found in Table 2. The parameter estimate foreducation is statistically insignificant….”If you move all tables and figures to the Appendix, the Appendix should have two separatesections: one for tables and one for figures. Tables and figures should not be copy-pasted from thesoftware output. You should create your own tables and graphs. Instructions for Section 5“Results”What independent variables should you include in your models?Sometimes you have a theory that determines which independent variables you should include inyour model (for example, you may try to quantify the parameters in a Cobb-Douglas productionfunction: the independent variables are labor and physical capital). In other cases, you do not havesuch a theory but you have a large set of independent variables that you believe can be used toexplain your dependent variable. In such a case you are not sure which variables you shouldinclude and which not. You can proceed in the following way: Begin with all independent variables that you think are relevant and that do not have nearmulticollinearity issues. If applicable, you may consider cross products of these variables(you need to justify why you did so). Estimate your model with all of them. Check the output. Some of the parameter estimates may be statistically insignificant.Consider the hypothesis that they are jointly insignificant (F-test). If so, remove theindependent variables associated with these parameters. Otherwise, consider all thehypotheses that are related to subgroups of these coefficients. Estimate your model again and repeat the hypothesis testing. The coefficient estimatesshould not be considerably different (especially their sign), compared to the previousmodels, or else you may have an omitted variables bias. Examine again if the OLSassumptions are satisfied.This procedure is called general-to-specific approach.In Section 5:1. Estimate your regression model as discussed above.2. What is the adjusted R-squared? Do you deem it to be large enough? Could you add moreindependent variables? Note that if you decide to explore the case of using moreindependent variables by including them in the“updated”model, please use the generalto-specific approach.3. Perform misspecification testing (RESET, Breusch-Pagan and White’s tests) in thefollowing order:If functional form is found to be a problem when using RESET, change thespecification by applying logarithms to suitable variables or by adding squared termsof some of the independent variables.Estimate the“updated”model and check again for functional form using RESET.Select the model that looks less misspecified.Check the“updated”model for heteroskedasticity (B-P and White’s tests).If heteroskedasticity is not an issue then you are ready to discuss your regressionoutput results.If heteroskedasticity is found then re-estimate your“updated”model using robuststandard errors.Note: In misspecification tests, the null hypothesis is that there is no problem with thespecification.

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