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MPHIL AdvancedEconometrics
Lecture 12
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Models for Discrete Choice
Greene Chapter 21 Gujarati Chapter 15
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Introduction
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Models for Binary Choice (Linear Probability Model)
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Linear Probability Model
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Non-Normality of Disturbances in Linear Probability Model
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Heteroscedastic Disturbances in Linear Probability Model
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R2 is No Longer a Good Measure of Fit
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Logit Model
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Logit Model
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Logit Model
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Logit Model
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Analysis of Binary Choice Models using Matrices
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Analysis of Binary Choice Models using Matrices
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Reformulation of Logit Model
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Estimation of the Logit Model
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Estimation of the Logit Model
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Estimation of the Logit Model
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Estimation of the Logit Model
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Estimation of the Logit Model
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Grouped Data
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Grouped Data
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Grouped Data
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Grouped Data
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Grouped Data
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Interpretation of Odds Ratio
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Probit Model
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Probit Model
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Probit Model
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Probit Model
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Probit Model
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Which Distribution Should We Use?
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Marginal Effects
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Marginal Effects
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Marginal Effects
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Comparing Logit and Probit Coefficients
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Comparing Logit and Probit Coefficients
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Comparing Logit and Probit Coefficients
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Comparing Logit and Probit Coefficients
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Tobit Model
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Tobit Model
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Tobit Model
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Count Data
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Count Data
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Count Data
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Count Data
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Other Limited Dependent Variable Models (Ordered Logit and Probit)
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Multinomial Logit and Probit
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Duration Models
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