Problem Set 1
The file beauty.dta collects information on wages, years of experience and gender for 1, 260
individuals. We also have information on a subjective index of beauty (looks) measured from
1 to 5.
lwage = β0 + β1exper + β2exper2 + β3looks + β4female + u (1)
Q1. Estimate the parameter of the econometric model in equation (1) using STATA. What is
the estimated effect of experience on wages?
Q2. What can we say about the effect of an increase of one point in the looks scale on wages?
Is this larger or smaller than the effect of an additional year of experience for an individual
with 5 years of experience? Is the difference statistically significant?
Q3. Is there any evidence of functional form misspecification for equation (1)?
Q4. How would you test the hypothesis that beauty has the same effect on wages both for
males and females? Provide a p-value for such test.
Q5. Is there any evidence of heteroskedasticity in equation (1)? Provide an heteroskedasticity
robust estimate of the parameters in equation (1).
Problem Set 2
The file approval.dta contains information on 78 months of data during the presidency of
George W. Bush spanning the period between February 2001 and July 2007. In particular
we have information on the approval rate for George W. Bush (approve), the logarithm of
consumer price index for food (lcpifood), the logarithm of the gas price (lrgasprice) as well
as several event dummies for the 9/11 terrorist attack (sep11 – equal to one for September
2001 and zero otherwise), Iraq invasion (iraqinvade – equal to one from March 2003 to June
2003 and zero otherwise) and hurricane Katrina (katrina – equal to one for September 2005
and zero otherwise).
Consider the following econometric model:
approvet = α0 + α1lcpifoodt + α2lrgaspricet + ut (2)
1
Q6. Using STATA obtain the OLS estimates for α1 and α2 and interpret the estimated coef-
ficients. Are they statistically significant? Do they have the sign you expected?
Q7. Explain why it would be safe to include a trend in the analysis. Consider both a linear
and a quadratic trend: which one provides a better fit to the data? (hint: the variable t
takes value 1 for the first observation and 78 for the last one)
Q8. Is there any evidence of the seasonality?
Q9. Do your conclusions in question 6 change when we control for major events (i.e. including
sep11, iraqinvade, katrina)? What was the impact of the 9/11 terrorist attack on
Bush’s approval rate according to this model?
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