The method of simulated scores (MSS) is presented for estimating LDV models with flexible correlation structure in the unobservables. We propose simulators that are continuous in the unknown parameter vectors, and hence standard optimization methods can be used to compute the MSS estimators that...

The nonparametric censored regression model is y = max[c, m(x) + e], where both the regression function m(x) and the distribution of the error e are unknown, but the fixed censoring point c is known. This paper provides a simple consistent estimator of the derivative of m(x) with respect to each...

This paper studies estimation and specification testing in threshold regression with endogeneity. Three key results differ from those in regular models. First, both the threshold point and the threshold effect parameters are shown to be identified without the need for instrumentation. Second, in...

An asymptotic theory is developed for a weakly identified cointegrating regression model in which the regressor is a nonlinear transformation of an integrated process. Weak identification arises from the presence of a loading coefficient for the nonlinear function that may be close to zero. In...

We present a simple way to estimate the effects of changes in a vector of observable variables X on a limited dependent variable Y when Y is a general nonseparable function of X and unobservables. We treat models in which Y is censored from above or below or potentially from both. The basic idea...

We consider an econometric model based on a set of moment conditions which are indexed by both a finite dimensional parameter vector of interest, and an infinite dimensional parameter, h, which in turn depends upon both and another infinite dimensional parameter, tau. The model assumes that the...

We consider identification in a "generalized regression model" (Han, 1987) for panel settings in which each observation can be associated with a "group" whose members are subject to a common unobserved shock. Common examples of groups include markets, schools or cities. The model is fully...

We present a simple way to estimate the effects of changes in a vector of observable variables X on a limited dependent variable Y when Y is a general nonseparable function of X and unobservables, and X is independent of the unobservables. We treat models in which Y is censored from above,...

This paper evaluates the impact of Round I of the federal urban Empowerment Zone (EZ) program on neighborhood level labor and housing market outcomes over the period 1994-2000. Using four decades of Census data in conjunction with information on the proposed boundaries of rejected EZs, we find...

We consider the problem of estimating the conditional distribution of a post-model-selection estimator where the conditioning is on the selected model. The notion of a post-model-selection estimator here refers to the combined procedure resulting from first selecting a model (e.g., by a model...