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 websitehttp://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  R 2 and F - statistic for the estimated model. You must use all the available explanatory variables for this run of the OLS model.

iii)  Interpret R 2    .

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 R 2 and F - statistic for this final regression.