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POLS 6482 ADVANCED MULTIVARIATE STATISTICS
Due 5 November 2001
EVIEWS dataset for the coffee example
discussed in Epple Notes VI-20 to VI-24
Coffee Data (coffee.wf1)
and paste it into STATA. The variable
cons is the per capita consumption of coffee in pounds,
price is the price in cents per pound,
pcinc is the per capita income in dollars, and
year is the year.
Turn in the d and
In this problem we will work with the Drinking Age and Highway
Fatality rate dataset discussed in Epple Notes X-4 to X-16.
Replicate the regressions shown on pages VI-20, VI-22, and the
Ramsey Reset tests shown on page VI-23.
Interpret the coefficients for the Log-Log model. Do the signs on the
coefficients make sense? Why? Why not?
Drinking Age and Highway Fatality
rate dataset (EVIEWS Dataset)
Download the dataset and bring it up in EVIEWS.
Reproduce the results shown on page X-5 to X-15. Specifically, run the
regression, do the White test, run the regression with the White Standard Error
Correction, and do the weighted regression.
In this problem we are going use the 1968 and 1996 NES presidential
election data from homework 2 to test whether or not the same linear model applies
to the party identification of men and women. Recall that the specification was:
Do the estimated coefficients make sense to you? Why or why not?
Paste the dataset into Stata, define the
variables appropriately, and turn in the d
and summ commands.
In Stata run the regressions shown on
pages X-5 and X-11. To do the standard error correction use the command:
regress lft18t20 tax drkage pcinc miles yngdrv insp mormon
prot cath sobab wet, robust
Produce the plot shown in Epple Notes X-6. In
Stata you can do it with the
predict yresid, residuals
plot yresid tax
The first command places the residuals into the vector
yresid and the second command produces a scatterplot with
tax as the horizontal axis with the residuals on
the vertical axis (note that this is backwards from
Party = f(income, race, sex, south, education, age)
or, expressed in terms of a regression equation:
y = party,
x1 = income,
x2 = race,
x3 = sex,
x4 = south,
x5 = education, and
x6 = age.
Use the method shown in Epple Notes VII-2 to VII-8 to test whether or
not women and men have different linear models for party identification. The
hypothesis test is the same as that shown on page VII-7. In this context the
indicator variable sex plays the same role as
the indicator variable DUM in the delivery dataset.
Do the hypothesis test in both
Stata for the 1968 and 1996 data.
Calculate the exact p-value
of the test using @fdist(f_stat, df_numerator, df_denominator) in
display fprob(df_numerator, df_denominator, f_stat) in
Stata. Show all of your regression output and
Discuss the substantive significance of these test results. Be specific.