Estimation of nonseparable models with censored dependent variables and endogenous regressors

In this article we develop a nonparametric estimator for the local average response of a censored dependent variable to endogenous regressors in a nonseparable model where the unobservable error term is not restricted to be scalar and where the nonseparable function need not be monotone in the unobservables. We formalize the identification argument put forward in Altonji, Ichimura, and Otsu (2012 Altonji, J. G., Ichimura, H., Otsu, T. (2012). Estimating derivatives in nonseparable models with limited dependent variables. Econometrica 80:1701–1719.[Crossref], [Web of Science ®] , [Google Scholar]), construct a nonparametric estimator, characterize its asymptotic property, and conduct a Monte Carlo investigation to study its small sample properties. Identification is constructive and is achieved through a control function approach. We show that the estimator is consistent and asymptotically normally distributed. The Monte Carlo results are encouraging.     

Optimal experimental design criterion for discriminating semiparametric models

This paper studies the optimal experimental design problem to discriminate two regression models. Recently, López-Fidalgo et al. [2007. An optimal experimental design criterion for discriminating between non-normal models. J. Roy. Statist. Soc. B 69, 231–242] extended the conventional T-optimality criterion by Atkinson and Fedorov [1975a. The designs of experiments for discriminating between two rival models. Biometrika 62, 57–70; 1975b. Optimal design: experiments for discriminating between several models. Biometrika 62, 289–303] to deal with non-normal parametric regression models, and proposed a new optimal experimental design criterion based on the Kullback–Leibler information divergence. In this paper, we extend their parametric optimality criterion to a semiparametric setup, where we only need to specify some moment conditions for the null or alternative regression model. Our criteria, called the semiparametric Kullback–Leibler optimality criteria, can be implemented by applying a convex duality result of partially finite convex programming. The proposed method is illustrated by a simple numerical example.     

Crime,Search, and Ex‐offenders’ Accessibility to Labor Markets

Rehabilitation for ex‐offenders has positive (e.g. more production) and negative (e.g. more crime) effects on society. We investigate the effect of rehabilitation on criminal behavior and the labor market using a search model. In the case where ex‐offenders cannot join the labor market, promoting rehabilitation for them may reduce crime. In such a case, the equilibrium allocation may be inefficient. Moreover, under the Hosios condition, equilibrium labor market tightness is lower than the efficient outcome because of crime, as the crime option raises the value of unemployed workers. Because this option works as a negative externality on matched firms, equilibrium tightness is lower than the optimal level.     

Robustness,Infinitesimal Neighborhoods,and Moment Restrictions

This paper is concerned with robust estimation under moment restrictions. A moment restriction model is semiparametric and distribution‐free; therefore it imposes mild assumptions. Yet it is reasonable to expect that the probability law of observations may have some deviations from the ideal distribution being modeled, due to various factors such as measurement errors. It is then sensible to seek an estimation procedure that is robust against slight perturbation in the probability measure that generates observations. This paper considers local deviations within shrinking topological neighborhoods to develop its large sample theory, so that both bias and variance matter asymptotically. The main result shows that there exists a computationally convenient estimator that achieves optimal minimax robust properties. It is semiparametrically efficient when the model assumption holds, and, at the same time, it enjoys desirable robust properties when it does not.     

禅宗公案的形成和公案禅的起源

禅宗公案是在漫长的历史过程中逐渐形成的。本文通过分析举话活动的源流、公案集的出现以及拈颂对公案形成所起的作用,来讨论禅宗公案的形成过程。实际上,公案是在宋代禅师阐释唐代禅师言行的过程中形成的。公案的形成为公案禅的产生准备了条件。     

Estimating Derivatives in Nonseparable Models With Limited Dependent Variables

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, below, or both. The basic idea is to first estimate the derivative of the conditional mean of Y given X at x with respect to x on the uncensored sample without correcting for the effect of x on the censored population. We then correct the derivative for the effects of the selection bias. We discuss nonparametric and semiparametric estimators for the derivative. We also discuss the cases of discrete regressors and of endogenous regressors in both cross section and panel data contexts.