**
POLS 6482 ADVANCED MULTIVARIATE STATISTICS
Ninth Assignment
Due 5 November 2001**

- Download the
**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**summ**commands.

- 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?

- Turn in the
- In this problem we will work with the Drinking Age and Highway
Fatality rate dataset discussed in Epple Notes X-4 to X-16.

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.

- 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 commands:

**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**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:

**Party = f(income, race, sex, south, education, age)**

or, expressed in terms of a regression equation:

**y = b**_{0}+ b_{1}x_{1}+ b_{2}x_{2}+ b_{3}x_{3}+ b_{4}x_{4}+ b_{5}x_{5}+ b_{6}x_{6}+ e

where**y = party, x**and_{1}= income, x_{2}= race, x_{3}= sex, x_{4}= south, x_{5}= education,**x**_{6}= 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
**EVIEWS**and**Stata**for the 1968 and 1996 data. Calculate thep-value of the test using*exact***@fdist(f_stat, df_numerator, df_denominator)**in**EVIEWS**and**display fprob(df_numerator, df_denominator, f_stat)**in**Stata**. Show all of your regression output and calculations.

- Discuss the substantive significance of these test results. Be specific.

- Do the hypothesis test in both