ECMT2150 In-Semester Test Part 2 Practice Questions
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ECMT2150 In-Semester Test Part 2 Practice Questions
Empirical Exercise Using STATA: Estimation, Interpretation and Inference
Data and research question description
Housing prices in Sydney have been increasing strongly for several years. This brings with it issues related to housing affordability and concerns about the sustainability of rising debt levels. We will investigate some potential drivers of house prices to help policy makers better understand what might be causing rising housing prices in Sydney. To do this, we will use observations on housing prices and housing characteristics collated from different suburbs in Sydney in the mid-1990. This is when housing prices in Sydney first started rising. The dataset, ‘syd_hprice.dta’, contains a sample of 88 observations on the following variables:
. price = house price, $1000s
. assess = assessed value, $1000s
. bdrms = number of bedrooms
. lotsize = size of the property in square metres
. sqrm = size of house in square metres
. fed = indicator variable, 1 if home is federation style, 0 otherwise
Questions:
a) Compute the mean and standard deviation of price, assess, lotsize and sqrm.
b) Create (and keep a copy of) two scatterplots. One with a plot of price (y-axis) against lotsize (x-axis) and the second one with a plot of the natural log of price (y-axis) against the natural log of lotsize (x-axis). Discuss the differences you observe in the two scatterplots.
c) Consider the following model of housing prices:
log(PTice) = β0 + β1 log(assess) + β2 log(lotsize) + β3 log(sqTm) + β4 bdTms + β5fed + u
Create the new explanatory variables in the model and estimate the model and report the results. Comment on the individual coefficient estimates and whether these are statistically significant and/or economically significant.
d) Interpret the estimated coefficient on the federation indicator (dummy) variable in the model. What does it suggest about the price of Federation-styled homes in Sydney?
e) What share of the variation in the log of housing prices across the sample of suburbs does the model explain? What does the R2 tell us about the reliability of our results?
f) Instead of, or in addition to, the R2, how should we assess the reliability of our results in the model?
g) Test the null hypothesis:
H0 : β2 = β3 = β4 = 0, β1 = 1
Against the alternative hypothesis that at least one of the restrictions is not valid. Show the F-statistic and P-value. What do you conclude about the model for housing prices?
h) Based on the results from g) we decide to estimate the following alternative model for Sydney housing prices:
log(PTice) = Y0 + Y1 log(assess) + Y2fed + ε
Report and interpret the results for this alternative model. What is the implication of this model for housing prices in Sydney?
2023-09-19