Reference no: EM13374704

General Instructions

The Modeling project for this course is intended to give you hands on experience to construct an econometric model for a real world problem. You must keep a copy of this project to show your prospective employers to substantiate the fact that you have learnt quite a lot of econometric modeling. They will really like it in your resume. However, in this project you are not able to involve yourself in the data collection effort, which is a major learning and exciting experience in any econometric analysis. The data that are being provided to you have the features described in the following section.

The modeling project Report must be typewritten, double-spaced, and must not exceed eight pages. The Report must not be in EXCEL sheet or in STATA sheet. Over and above the 8-page limit, you must attach STATA print out of the regression results as APPENDIX. On your title page, you should have the name of the course the semester (for instance, Spring 2012), the nice title you have decided to give to your report, and your name.

You are an economist at the headquarters of a major real estate company interested in the Chicago urban area. Your task is to investigate the effects of various structural, locational, access factors and factors relating to the local government spending on home value. Your programming assistant has compiled data for a randomly selected sample of about 2000 property transactions from Cook and Dupage counties of the Chicago Metropolis.

- Explain, in your own words, what economic issues you are addressing in the project.
- Explain, in your own words, why the subject may be interesting.
- Discuss, in specific terms, what you wish to predict or explain (the subject of your paper).
- Explain the dependent and each of the explanatory variables. Specify the units in which they are measured.
- Write down, before doing any estimation, the original population regression model with SPRICE, NROOMS, LVAREA, HAGEEFF, LSIZE, PTAXES, MEDINC, DFCL, SSPEND, MSPEND in natural logarithm form. Keep the rest of the variables in unlogged form, since they have zero values in the sample.
- Discuss how you expect each of your explanatory variables to influence the dependent variable (i.e., positive or negative relationship). You must explain why you expect so.

2.
i) State (mathematically and in words), all the assumptions you need to make in order to estimate the model.
ii) Write out the estimated regression equation for the first computer run, with standard errors in parenthesis under each coefficient. Also, present statistic - F and 2R for the estimated model. You must use all the available explanatory variables for this run of the OLS model.
iii) Interpret 2R .
iv) Perform a test of the overall significance of the regression equation (F-test for the full set of regression parameters). Provide all the details of the test, including decision and conclusion.
v) Perform the test to see if the variable hageeff. is statistically significant at 5% level. Provide all the details of the test.
vi) Drop the insignificant variables, one at a time, by looking at the p-value from the regression results. This means you need to drop the one with the highest p-value, then run the regression, look for the highest p-value again, then drop the associated variable....and continue this way until all coefficients are significant at the 0.05 level of significance.

vii) Now do the subset test. That is, using the full regression model from (ii) and the final model obtained in (v), test whether the variables you dropped are significant as a group, using F-test for the subset of the explanatory variables you finally keep. Rejection of the null hypothesis would suggest that you might have dropped an important variable and you
should reconsider including one or more variables you have dropped earlier.

viii) Write out your final regression equation, with standard error in parentheses under each coefficient. Also, present statistic - F and 2R for this final regression.

3.

The following pertains to the revised model (i.e., after dropping all the insignificant explanatory variables), or pertains to the original model if no revisions were made:

- Interpret three most highly significant estimated regression coefficients in the context of the problem.
- Choose two explanatory variables from the final regression and construct and interpret the confidence intervals for the population coefficients of your chosen explanatory variables.

4. Conclusion
- State in your own words your conclusions regarding the model(s) you have estimated.
- Carefully review in a paragraph the original and the revised models.
- Discuss any problems your model might have. Do not hesitate to write the strengths and weaknesses of your model and your results.
- Finally, offer any interesting implications of your findings that you might convey to your boss in a non-technical way.