Some Small Sample Properties of Durbin's Tests for Serial Correlation in Regression Models Containing Lagged Dependent Variables

Econometrica ◽  
1974 ◽  
Vol 42 (4) ◽  
pp. 763 ◽  
Author(s):  
James L. Kenkel
Keyword(s):  
Regression Models ◽  
Serial Correlation ◽  
Small Sample ◽  
Dependent Variables ◽  
Small Sample Properties

scholarly journals An Estimator for Some Binary-Outcome Selection Models Without Exclusion Restrictions

2003 ◽  
Vol 11 (2) ◽  
pp. 111-138 ◽  
Author(s):  
Anne E. Sartori
Keyword(s):  
Small Sample ◽  
Likelihood Estimator ◽  
Selection Models ◽  
Explanatory Variables ◽  
Dependent Variables ◽  
Outcome Selection ◽  
Small Sample Properties ◽  
Game Theoretic ◽  
Distributional Assumptions ◽  
Exclusion Restrictions

This article provides a new maximum-likelihood estimator for selection models with dichotomous dependent variables when identical factors affect the selection equation and the equation of interest. Such situations arise naturally in game-theoretic models where selection is typically nonrandom and identical explanatory variables influence all decisions under investigation. When identical explanatory variables influence selection and a subsequent outcome of interest, the commonly used Heckman-type estimators identify from distributional assumptions about the residuals alone. When its own identifying assumption is reasonable, the new estimator allows the researcher to avoid the painful choice between identifying from distributional assumptions alone and adding a theoretically unjustified variable to the selection equation in a mistaken attempt to “boost” identification. The article uses Monte Carlo methods to compare the small-sample properties of the estimator with those of the Heckman-type estimator and ordinary probit.

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