This site is an archived version of Voteview.com archived from University of Georgia on

**
POLS 6482 ADVANCED MULTIVARIATE STATISTICS
Eighth Assignment
Due 29 October 2001**

- This problem is a continuation of 1.c of the 3
^{rd}homework, 2 of the 4^{th}homework, and 2 of the 5^{th}homework. I made some additional corrections in the file so download the new**Stata**file below:

105th Congressional District Data (HDMG105ZZ.DTA)

Note that I have added two variables**bush00**and**gore00**that are the Bush and Gore 2000 election percentages in the congressional district.

- Download the following text file:

106th House DW-NOMINATE Scores (H106.TXT)

and use**Epsilon**to paste H106.TXT into**Stata**. Use the following variable names and definitions:

Follow the instructions shown in part 1 of Homework 3 to merge this file into HDMG105ZZ.DTA. Sort on**cong2**byte %8.0g congress no.**icpsrid2**long %12.0g id no (icpsr and Poole/Rosenthal)**state**byte %8.0g icpsr state code**district**byte %8.0g cong. district no.**statenm3**str7 %9s name of state**party2**int %8.0g political party**name3**str11 %11s name of member**dwnom1n**float %9.0g 1st dim. dw-nom. 106th**dwnom2n**float %9.0g 2nd dim. dw-nom. 106th**state**and**district**in both files! Rename your**STATA**file**HDMG106.DTA**, do the**d**and**summ**commands, and report the results.

- In
**STATA**run the regressions:

**regress clint96 black south hisp income dwnom1 dwnom2**

**regress gore00 black south hisp income dwnom1n dwnom2n**

**regress dole96 black south hisp income dwnom1 dwnom2**

**regress bush00 black south hisp income dwnom1n dwnom2n**

Compare and contrast the results of these four regressions. What do you think accounts for the differences between the 1996 and 2000 results. Be specific.

- In
**STATA**run the regressions:

**regress bush00 black hisp income dwnom1n dwnom2n if south==0**

**regress bush00 black hisp income dwnom1n dwnom2n if south==1**

where "south==0" selects the congressional districts in the North, and "south==1" selects the congressional districts in the South (recall that South is defined as the 11 states of the Confederacy plus Kentucky and Oklahoma).

Interpret the results of these two regressions. What do you think accounts for the differences? Be explicit.

- Paste
**HDMG106.DTA**into**EVIEWS**and replicate the regional Bush 2000 vote regressions in (c). To do this, sort the dataset using the**south**variable, use the**SHOW**command to find the observation number of the last northern district, and then issue the command:

**SMPL 1 XXX**

where "XXX" is the observation number of the last northern district. (This command tells**EVIEWS**to use observations 1 to XXX -- SMPL stands for "sample".) Now, run the regression:

**LS BUSH00 C BLACK HISP INCOME DWNOM1N DWNOM2N**

To get the southern regression simply change the sample range and run the same regression; that is:

**SMPL XXY 435**

**LS BUSH00 C BLACK HISP INCOME DWNOM1N DWNOM2N**

where "XXY" is the observation number of the first southern district. For example, if "XXX" was 200 then "XXY" is 201.

- Use the
**SORT**,**SHOW**, and**SMPL**commands in**EVIEWS**to run the regression

**LS BUSH00 C BLACK SOUTH HISP INCOME DWNOM1N DWNOM2N**

on congressional districts with 9% or less Blacks and 25% or greater Blacks. Interpret the results of these two regressions. What do you think accounts for the differences? Be explicit.

- Download the following text file:
- This problem is a continuation of problem 2 on the 6
^{th}homework using the Cigarette data discussed in Epple Notes V.

- Use
**EVIEWS**to Perform the Chow Breakpoint test discussed on pages V-8 to V-12 of the Epple notes. Replicate the calculations shown on V-10 and use

**scalar pval=@fdist(_,_,_)**

to get the P-Value (report the first 10 digits). Replicate the method discussed on V-12.

- Perform the Chow Breakpoint test in
**STATA**. To replicate the tables shown on page V-11 use the commands:

**regress carmon nicot tar weight if litedum==0**

**regress carmon nicot tar weight if litedum==1**

**regress carmon nicot tar weight**

The p-value can be computed with the command:

**display fprob(df_num,df_denom,f_stat)**

Below is the output for the first command. Note that the Residual Sum of Squares is what you need to do the calculations shown on page V-10.**. regress carmon nicot tar weight if litedum==0 Source | SS df MS Number of obs = 18 ---------+------------------------------ F( 3, 14) = 68.15 Model | 467.096051 3 155.698684 Prob > F = 0.0000 Residual | 31.9839391 14 2.28456708 R-squared = 0.9359 ---------+------------------------------ Adj R-squared = 0.9222 Total | 499.07999 17 29.3576465 Root MSE = 1.5115 ------------------------------------------------------------------------------ carmon | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------+-------------------------------------------------------------------- nicot | -2.282413 5.320555 -0.429 0.674 -13.69387 9.129043 tar | .9897387 .3369055 2.938 0.011 .2671483 1.712329 weight | -4.604357 4.923935 -0.935 0.366 -15.16515 5.956433 _cons | 6.669547 4.326866 1.541 0.146 -2.610657 15.94975 ------------------------------------------------------------------------------** - Replicate the Chow Forecast Test discussed on pages V-13 to
V-16. To do this, sort the data using the
**tar**variable:

**sort tar**

Note that the last 3 observations will be the highest tar cigarettes. Go into the data editor in**STATA**and find the value of the 23^{rd}observation for**tar**and use it in the following:

**regress carmon nicot tar weight if tar < XX.X**

where "XX.X" is the value of**tar**. The results should allow you to replicate the test given on page V-14.

Compute the F-statistic and calculate the P-Value using**EVIEWS**.

- Use