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**
POLS 6482 ADVANCED MULTIVARIATE STATISTICS
Eleventh Assignment
Due 19 November 2001**

- In this problem we going to apply
**probit**,**logit**, and**linear probability**to some data from the 1968, 1996, and 2000 NES Presidential election surveys. These estimation methods are designed fordependent variables. The 1968 and 1996 data are similar to the data we used in part (1) of the 2*binary*^{nd}assignment. The variables are:

The data are in three text files:**Party Identification: 0=strong democrat 1=weak democrat 2=lean democrat 3=independent 4=lean republican 5=weak republican 6=strong republican Family Income: Raw Data (we will not use this variable) Family Income Quintile: 1 is the lowest quintile, 5 is the highest Race: 0 = White 1 = Black Sex: 0 = Male 1 = Female South: 0 = North 1 = South Education: 1 = High School or less 2 = Some College 3 = College degree Age: In Years Presidential Vote: 0 = Did Not Vote 1 = Voted for Democratic Candidate For President 2 = Voted for Republican Candidate For President 3 = Voted for 3**^{rd}Party Candidate for President

1968 Data

1996 Data

2000 Data

- Download these three files and
load them into
**EVIEWS**and**Stata**. Turn in the**d**and**summ**commands for all three datasets.

- In
**Stata**, a binary dependent variable is always defined as 0 being the "negative" outcome with all other nonmissing values being the "positive" outcome. Use**Presidential Vote**as a dependent variable with the remaining variables as independent variables; that is, run the following model on all three election years:

**probit voted party income race sex south education age**

You can interpret the probit coefficients roughly the same way that you interpret the regular multiple regression coefficients. A positive**b**means that the independent variable is_{j}of a "positive" ("negative") outcome. Compare the results for all three elections. What is your interpretation of the coefficients (what do they tell you about American Politics)? Be Specific.*increasing (decreasing) the probability*

- In
**EVIEWS**, a binary dependent variable is always defined as 0 being the "negative" outcome and 1 being the "positive" outcome. Create a dependent variable from**Presidential Vote**where 0 = Voted for the Democratic Party Candidate and 1 = Voted for Republican Party Candidate (note that non-voters and 3^{rd}party voters are missing data!). Run the following**logit**model on all three election years:

**logit y c party income race sex south education age**

You can interpret the logit coefficients roughly the same way that you interpret the regular multiple regression coefficients. A positive**b**means that the independent variable is_{j}of a "positive" ("negative") outcome. Compare the results for all three elections. What is your interpretation of the coefficients (what do they tell you about American Politics)? Be Specific.*increasing (decreasing) the probability*

**Linear Probability**is simply regular regression with the**White Standard Error Correction**applied to a binary dependent variable. Replicate the estimations of part (c) using linear probability in**EVIEWS**. To compare the logit and linear probability coefficients, normalize the**b**'s (except for_{j}**b**) so that their sum of squares is equal to 1. That is, square the k_{0}**b**'s, add them up, take the square root of this sum, and divide through the_{j}**b**'s by this number. Make a table showing these normalized_{j}**b**'s (except for the intercept term) and their p-values for the two models._{j}

- Download these three files and
load them into
- In this problem we are going to apply
**ordered probit**to the three Presidential election datasets. An ordered probit estimation is designed for a dependent variable with multiple categories where it is reasonable to assume that the categories can be. For example, the*rank ordered***party**variable ranges from 0 to 6 where 0 = strong democrat, 1 = weak democrat, 2 = lean democrat, 3 = independent, 4 = lean republican, 5 = weak republican, 6 = strong republican.

- Run the standard regression
of
**Party**on**income**,**race**,**sex**,**south**,**education**, and**age**in both**Stata**and**EVIEWS**.

- In
**EVIEWS**, to run an**ordered probit**issue the command:

**ordered party income race sex south education age**

In**EVIEWS**, to run an**ordered logit**issue the command:

**ordered(d=L) party income race sex south education age**

In**Stata**to run an**ordered probit**issue the command:

**oprobit party income race sex south education age**

In**Stata**to run an**ordered logit**issue the command:

**ologit party income race sex south education age**

Note the absence of**C**in the**EVIEWS**ordered probit and logit commands. As I will explain in class, the intercept term is picked up by the estimation of the cutting points on the latent dimension of the dependent variable. In**EVIEWS**if you issue the command:

**ordered party c income race sex south education age**

you will get exactly the same answer.

Make a table for each election showing theand their P-Values for all three models -- regular regression, ordered probit, and ordered logit.*normalized coefficients*

**EVIEWS**has two nice tables that you can produce for the ordered probit table. Under the**View**button on the probit table results you will find two options --**Dependent Variable Frequencies**and**Expectation-Prediction Table**. The former is simply a table of the frequencies and is self-explanatory. The latter contains the**predicted categories**(3^{rd}column in the table) for the dependent variable. Interpret the results shown in the**Expectation-Prediction Tables**corresponding to the three ordered probit estimations.

- Run the standard regression
of