Econ 312: Modeling Project 
 
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, Summer 2023), the nice 
title you have decided to give to your report, and your name. 
 
 
Data Description 
 
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. 
 
The data set for this project is up on the Canvas site. You need only to download the data set 
assigned to you. 
 
 
The details of the data, such as variable descriptions, original source, units in which they are 
measured are available in the library or on a specific Internet site. You need to have them ready 
before you start working on your modeling project. Do the following: 
• Go to the SFSU Library website http://www.library.sfsu.edu/. 
• Under OneSearch write Sudip Chattopadhyay Land Economics, then click search. 
• Choose the first article in the journal Land Economics, volume 75, number 1, pp. 22-38, 
1999. 
• When you download a PDF copy of the journal article, look for Table 3 in the article for 
variable definition, source, etc. 
 
 Instruction on the Modeling Project Write Up 
 
1. 
 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 (variables that take “0” values cannot be logged). 
 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 and F - statistic
2 R for the estimated 
model. You must use all the available explanatory variables for this run of the OLS model. 
iii) Interpret 
2 R . 
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 (i.e., the test for linear restrictions). That is, using the full regression 
model from (ii) and the final model obtained in (vi), 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 and F - statistic
2 R 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. 
 
 
5. 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. 
 
4. Complete Report (8 pages maximum) and Appendix printouts 
 Write out the complete report in maximum of 8 pages. 
 Attach as pages 9, 10, etc. the STATA printout of the full-set and the final regressions, to the 
report. No data set print out please. 
 Write your name on each page of the printout AND MAKE A PDF COPY OF THE ENTIRE 
REPORT INCLUDING THE APPENDIX. 
 Upload the PDF copy of the report on Canvas. 
 
 
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