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Dummy Endogenous Variables in a Simultaneous Equation System
TLDR
In this article, the authors considered the formulation and estimation of simultaneous equation models with both discrete and continuous endogenous variables and proposed a statistical model that is sufficiently rich to encompass the classical simultaneous equation model for continuous endogenous variable and more recent models for purely discrete endogenous variables as special cases of a more general model.Abstract:
This paper considers the formulation and estimation of simultaneous equation models with both discrete and continuous endogenous variables. The statistical model proposed here is sufficiently rich to encompass the classical simultaneous equation model for continuous endogenous variables and more recent models for purely discrete endogenous variables as special cases of a more general model.read more
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Identification Problems in the Social Sciences
TL;DR: In this paper, the authors synthesize some of their recent research and thinking on identification problems in the social sciences, including extrapolation of regressions, selection problem, identification of endogenous social effects, and identification of subjective phenomena.
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Identification Problems in the Social Sciences.
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Does Marriage Really Make Men More Productive
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Endogeneity in nonparametric and semiparametric regression models
Richard Blundell,James L. Powell +1 more
TL;DR: In this article, the authors consider the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors and identify the "average structural function" as a parameter of central interest.
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Iterative Estimation of a Set of Linear Regression Equations
TL;DR: In this article, the Gauss-Seidel method is used to estimate the coefficients of a set of linear regression equations, such that the explanatory variables are non-stochastic and the random disturbances of at least one pair of equations are correlated.
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The theory of correlation between two continuous variables when one is dichotomized
TL;DR: The problem of biserial correlation arises when one is sampling from a bivariate normal population in which one of the variables has beeii dichotomized, giving rise to only two observable values, say 0 and 1, and one wishes to use this dichotomised sample to estimate, or to test hypotheses concerning, the correlation coefficient p of the original bivariate norm distribution.