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Copyright © 2014 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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Presentation on theme: "Copyright © 2014 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education."— Presentation transcript:

1 Copyright © 2014 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education. Chapter 12 Limited Dependent Variables

2 12-2 Learning Objectives Estimate models with binary dependent variables Estimate models with categorical dependent variables

3 12-3

4 12-4 Binary Dependent Variables A limited dependent variable is a dependent variable whose range of possible values is restricted in some way. A binary dependent variable is a dependent variable that only takes on two possible values: 1 if the observation possesses a certain characteristics and 0 otherwise.

5 12-5 Binary Dependent Variables Example: Q:Do you have employer-provided health insurance? 1=Yes 0 = No Data: 38,659 individuals drawn from the National Health Interview Survey

6 12-6 Linear Probability Model The linear probability model is an ordinary least square (OLS) regression model with a binary dependent variable.

7 12-7 Linear Probability Model The linear probability model is an ordinary least square (OLS) regression model with a binary dependent variable. The issue: OLS estimates values for the dependent variables that are less than 0 and greater than 1 when such values cannot actually occur. Estimated but cannot actually occur

8 12-8 A More Preferred Estimator A more preferred estimator in the case of binary dependent variables would be one that did not estimate values less than 0 or greater than 1 and that allows the marginal effects to change as the values of the independent variables change.

9 12-9 The Logit Model The logit model is a popular specification for estimating population regression models with binary dependent variables. Notes: The Logit model cannot be estimated in Excel The initial estimates in a more-advanced package are coefficient estimates not marginal effects We should convert the coefficient estimates to marginal effects before interpreting them

10 12-10 The Logit Model Example:

11 12-11 The Logit Model

12 12-12 The Probit Model The probit model is a popular specification for estimating population regression models with binary dependent variables. Notes: The Probit model cannot be estimated in Excel The initial estimates in a more-advanced package are coefficient estimates not marginal effects We should convert the coefficient estimates to marginal effects before interpreting them Probit Function

13 12-13 The Probit Model Example:

14 12-14 The Probit Model

15 12-15 Comparison of the Models

16 12-16 Categorical Dependent Variables A categorical dependent variable is a dependent variable that takes on a limited, and usually fixed, number of possible values.

17 12-17 Categorical Dependent Variables Example: Q:Do you have enough food to eat? 2=Always 1 =Sometimes 0 = Never Data: 17,723 individuals drawn from the National Health and Nutrition Examination Survey

18 12-18 The Multinomial Logit The multinomial logit is a regression model that generalizes logistic regression to allow more than two discrete outcomes for the dependent variable. Note: The Multinomial Logit model cannot be estimated in Excel The initial estimates in a more-advanced package are coefficient estimates not marginal effects We should convert the coefficient estimates to marginal effects before interpreting them

19 12-19 The Multinomial Logit For example, Do not interpret these directly Convert them to marginal effects first

20 12-20 The Multinomial Logit

21 12-21 The Multinomial Logit

22 12-22 The Ordered Probit The ordered probit a regression model that generalizes the probit model to allow more than two discrete outcomes for the dependent variable. To be the appropriate model the outcomes of the dependent variable must be ordinal. Note: The Ordered Probit model cannot be estimated in Excel The initial estimates in a more-advanced package are coefficient estimates not marginal effects We should convert the coefficient estimates to marginal effects before interpreting them

23 12-23 The Ordered Probit For example, Do not interpret these directly Convert them to marginal effects first

24 12-24 The Ordered Probit

25 12-25 The Ordered Probit

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