Microsoft Word - Assignment 1 due 28 Aug 2020.docx
DUE FRIDAY 28 August BY 11.59PM
WORTH 10% OF COURSE ASSESSMENT and is worth total 30 points.
Late assignments will attract a penalty of 3 points per day late.
1. Your assignment must be submitted via MyUni in both Word and PDF formats.
2. Your assignment must be typed, including any equations using the equation editor in
3. Any graphs/tables generate in Stata must be pasted into your document from Stata.
DO NOT TAKE PHOTOS WITH YOUR PHONE AND PASTE THEM INTO YOUR DOCUMENT.
4. You must submit your log file and do file as separate documents.
The data file collegetown.dta contains observations on 500 single-family houses sold in
Baton Rouge, Louisiana, during 2009–2013. We are interested in the determinants of house
prices. The data include sale price (in thousands of dollars), PRICE, and total interior area of
the house in hundreds of square feet, SQFT, the age of the house measured as a categorical
variable (AGE), with 1 representing the newest and 11 the oldest, and several qualitative
Using Stata, please answer the following questions:
1. Generate summary statistics and graphs (eg. scatter plots, histograms) for the
variables, comment on the results and on anything you may find unusual or
2. Generate a new variable which is the natural log of price and create a histogram of
this new variable. Compare it to a histogram of the original variable price. Comment
on any differences.
3. Calculate summary statistics for PRICE and SQFT for homes close to Louisiana State
University (CLOSE = 1) and for homes not close to the university (CLOSE = 0). What
differences and/or similarities do you observe?
4. You are required to estimate several simple regressions to analyse the determinants
of sale price. Your regressions should also include models that capture any non-
linear relationship between sale price and other variables you think are appropriate.
This should include both a log linear model and quadratic model.
5. For each econometric model, report and interpret your regression results. Are any of
your estimates statistically different from zero?
6. Using the quadratic regression model ????? = ?! + ?"????" + ?, compute the
marginal effect of an additional 100 square feet of living area in a home with 2000
square feet of living space. Interpret this effect. What about for houses with 4000
square feet. Interpret this effect. What do you conclude?
7. For the model in part (6), compute the elasticity of PRICE with respect to SQFT for a
home with 2000 square feet of living space. Interpret this elasticity. What about for
houses with 4000 square feet? Interpret this effect. What do you conclude?
8. Write a
ief conclusion summarizing your results and discuss any implications of
your econometric results.