estimation of panel data models with censored

This paper considers the nonparametric identification and estimation of the average effect of a dummy endogenous variable in nonseparable models. The analysis includes the case of a dummy endogenous variable in a discrete choice model as a special case. This paper establishes conditions under which

Weak instruments arise when the instruments in linear instrumental variables (IV) regression are weakly correlated with the included endogenous variables. In generalized method of moments (GMM), more generally, weak instruments correspond to weak identification of some or all of the unknown

According to endogenous growth theory, permanent changes in certain policy variables have permanent effects on the rate of economic growth. Empirically, however, U. S. growth rates exhibit no large persistent changes. Therefore, the determinants of long-run growth highlighted by a specific growth

This paper considers a classical linear simultaneous equations model with random coefficients on the endogenous variables. Simultaneous equations models are used to study social interactions, strategic interactions between firms, and market equilibrium. Random coefficient models allow

This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional

Abstract. In additive error models with a discrete endogenous variable identi cation cannot be achieved under a marginal covariation condition when the support of instruments is sparse relative to the support of the endogenous variable. An iterated covariation condition with a weak montonicity

). VY formulate a fully nonparametric model featuring a nested weakly separable structure in which an endogenous regressor is binary–valued. The extension of the JPX approach considered here allows for nonbinary–valued discrete endogenous regressors and requires weaker support conditions than VY

We set out a Gibbs sampler for the linear instrumental-variable model with normal errors and normal priors, and we show how to compute the marginal likelihood. 1

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