his project is for my introduction to econometrics class. It's about a modeling project report that must be typewritten, double-spaced, and must not exceed eight pages. The Report must not be in EXCEL sheet or in a STATA sheet. I must attach STATA print out of the regression results as APPENDIX in step 5. This project is made out of five steps and require the use of the STATA application.Please refer to the Econometrics Project Instructions pdf for more detailed instructions.
Modeling Project Introduction to Econometrics: 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 2021), 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. Only use the data set assigned to you. (Data set has been atatched) 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. • Attached is the following article: Written by Sudip Chattopadhyay 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. Introduction: In this section, write a paragraph or two explaining/discussion the following aspect of your project. 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 the population regression equation as follows: 2. Data and Method In this section, describe the data by explaining what each variable represents by bringing the context of the Chicago Metropolitan area counties. Also, describe the econometric model you want to estimate and the relationship (positive or negative) you would think each variable has on the selling price of homes. log(sprice) = β0 + β2 log(nrooms) + β3 log(lvarea) + β4 log(hageeff) + β5 log(lsize) + β6 aircon + β7 nbath + β8 garage + β9 log(ptaxes) + β10 pctwht + β11 log(medinc) + β12 log(dfcl) + β13 dfni + β14 log(sspend) + β15 log(mspend) + β16 cook + β17 ohare + u (Note that in the above regression model SPRICE, NROOMS, LVAREA, HAGEEFF, LSIZE, PTAXES, MEDINC, DFCL, SSPEND, MSPEND are transformed in to natural logarithm. Keep the rest of the variables in unlogged form, since they have zero values in the sample. Transformation into natural logarithm is required before you start estimating the above population model in STATA.) 3. Empirical Results In this section, you must do the following: 3.1 Full Regression In this sub-section, you must present and discuss the full regression estimated model with all the available explanatory variables. i) Present 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. ii) Interpret 2R . iii) 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. iv) Perform the test to see if the variable hageeff is statistically significant at 5% level. Provide all the details of the test. 3.2 Final Regression In this sub-section, present and discuss the final model by carrying out the following steps: v) 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. vi) Now do the subset test. 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. vii) Presnt your final regression equation, with standard error in parentheses under each coefficient. Also, present statistic-F and2R for this final regression. 3.3 Analysis and Inferences In this sub-section, present the following analyses/inferences pertaining to the revised model (i.e., after dropping all the insignificant explanatory variables) 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 each of your chosen explanatory variables. 4. Discussion and Conclusion (one or two paragraph) In this section, provide a wholistic discussion of the results, in general. Then conclude with your own observations on your findings. State in your own words your conclusions regarding the final (revised) model you have estimated. Base your conclusion by carefully reviewing the final (revised) models and the causal relationships you observe in your model. Discuss any problems your final model might have. Do not hesitate to write the strengths and weaknesses of your final model and your results. Finally, offer any interesting implications of your findings that you might like to convey to your boss in a non-technical way. For example, based on your findings in sub-section 3.3. 5. Appendix (Computer printout) In this section, include STATA printout of the full-set and the final regressions. No dataset print out please. General Instructions Estimating the Demand for Air Quality: New Evidence Based on the Chicago Housing Market Sudip Chattopadhyay ABSTRACT. This paper combines a new, large household-level data set with the two-stage he- donic-estimation technique to derive new esti- mates of willingness to pay (WTP) for reduced air pollution. The WTP estimates are found robust against functional-form specification. Marginal WTP estimates for a reduction in particulate mat- ter (PM-10) are found to be quite comparable with some previous estimates. Benefits of non- marginal changes exhibit consistently higher monetary returns in the case of PM-10 than in the case of SO2, signifying that households dislike particulate pollution more than they do sulfur. (JEL Q25) I. INTRODUCTION Measuring the welfare impact of environ- mental degradation, particularly air pollu- tion, using hedonic techniques has remained an important area of empirical research in the past few decades. The use of hedonic benefit estimates of clean air is no longer limited to addressing welfare issues but extends to in- clude such important aspects as incorporat- ing monetary values for changes in environ- mental quality into national accounts (Smith and Huang 1995). After the theoretical paper by Rosen (1974) developing the hedonic model, there have been numerous empirical studies which estimate willingness to pay (WTP) for marginal changes in air quality. Unfortunately, studies that estimate the envi- ronmental benefits of non-marginal changes in air quality are so far, very limited. Such estimates, which are needed to gauge the benefits of large changes in air quality, re- quire knowledge of the parameters of the consumer utility function. Deriving such pa- rameter estimates requires the application of the hedonic two-stage estimation technique on household-level data. The present paper combines a new, large household-level data set with the two-stage estimation technique to derive new estimates of WTP for reduced air pollution. The study models the Chicago housing market to estimate the demand for clean air measured in terms of concentration of partic- ulate matter (PM-10) and sulfur dioxide (SO2). There have been a few studies that es- timate WTP for reduced air pollution in the Chicago housing market (see, e.g., Atkinson and Crocker 1987; Bender, Gronberg, and Hwang 1980). But these studies have limited appeal, for two reasons. First, the hedonic data sets considered in these studies pertain to the 1960s or the early 1970s. Second, the estimates are only for marginal changes in air quality, which are not useful for welfare analysis (see, e.g., Bartik 1988; Palmquist 1988, for discussion of exact measurement of welfare). Since Chicago falls in the desig- nated non-attainment region by National Ambient Air Quality Standards (NAAQS) (National Air Quality Emissions Trend Re- port 1990), it is worthwhile to carry out he- donic analysis with more recent data to estimate WTP for both marginal and non- marginal changes in air quality. The present research compares the new estimates of mar- ginal benefits with the estimates obtained in some previous studies. Using the estimates of non-marginal benefits from the second-stage hedonic regression, the study also analyzes the size of the monetary returns to reduced The author is with the Department of Economics, Kansas State University. He wishes to thank Jan Brueckner, his thesis adviser, for advice and guidance. Thanks are due to John Braden for his comments on an earlier version of the paper and Robert Shaw at Housing and Urban Development Office, Washington, DC, for providing the data on housing, and two anonymous ref- erees for their helpful suggestions that improved the paper. Land Economics • February 1999 • 75 (1): 22-38 75(1) Chattopadhyay: Demand for Air Quality in Chicago 23 air pollution, the knowledge of which is nec- essary for important policy discussions. Two major econometric issues that must be addressed in reliable estimation of the he-