• Read the instructions carefully. Up to ten (10) points out of a total of 100 will be deducted for each
non-compliance. Show all your work.
• Submit one document with no more than 6 pages of writing. All codes and output should be in appendices that do
not count toward the 6-page limit. There should be 3 appendices:
oOutput in table and figure formats. Tables and figures should be numbered and labeled.oCode for your main analyses
o Data cleaning/preparation code
• The file should be submitted. For any work submitted after the deadline 10 points will be deducted. Submission
platform will be closed.
• You may clean data in groups. Groups need to identify themselves and members.
Data:
Consumer Expenditure Survey: http://www.bls.gov/cex/pumdhome.htm
Data files in csv format: http://www.bls.gov/cex/pumd/data/comma/intrvw13.zip Data documentation is available under Most
Recent PUMD Release at http://www.bls.gov/cex/pumdhome.htm.
Note that the data is also available in SAS, SPSS, and STATA formats.
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1. Housing type choice
a. Build a model that best predicts housing type choice. What is the best model? Why is it the best?
b. What are the steps you take to get the model in a.? Explain in detail.
c. What are the top 3 determinants of housing type choice? Do you use the model in a. to answer this question? If
yes, why? If no, why not? Explain how you decide which determinants are most important.
2. Determinants of income
a. What are possible techniques that allow you to study determinants of income?
b. How do you determine features in your models?
c. Which model do you prefer? Why? What are the top 3 determinants of income?
d. Let Xbe an independent variable/feature. Can you use your model in c. to answer how much income will change given
a one unit increase in X? Explain your reasons. If your answer is no, build a model that would let you answer the
question.
e. Interpret the results from the model in d. (or the model in c. if you did not build a new model in d.).
f. Distinguish between endogenous and exogenous variables in your model. Why might this distinction be important?
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