This paper investigates how enforcement of labor regulation affects the firm's use of informal employment and its impact on firm performance. Using firm level data on informal employment and firm performance, and administrative data on enforcement of regulation at the city level, the authors...

A two-step generalized method of moments estimation procedure can be made robust to heteroskedasticity and autocorrelation in the data by using a nonparametric estimator of the optimal weighting matrix. This paper addresses the issue of choosing the corresponding smoothing parameter (or...

We propose a simple model selection test for choosing among two parametric likelihoods which can be applied in the most general setting without any assumptions on the relation between the candidate models and the true distribution. That is, both, one or neither is allowed to be correctly...

In this article, we develop a general method for testing threshold effects in regression models, using sup-likelihood-ratio (LR)-type statistics. Although the sup-LR-type test statistic has been considered in the literature, our method for establishing the asymptotic null distribution is new and...

In this paper, we propose a general method for testing inequality restrictions on nonparametric functions. Our framework includes many nonparametric testing problems in a unied framework, with a number of possible applications in auction models, game theoretic models, wage inequality, and...

We present the clrbound, clr2bound, clr3bound, and clrtest commands for estimation and inference on intersection bounds as developed by Chernozhukov, Lee, and Rosen (2013, Econometrica 81: 667–737). The intersection bounds framework encompasses situations where a population parameter of...

The estimation problem in this paper is motivated by the maximum score estimation of preference parameters in the binary choice model under uncertainty in which the decision rule is affected by conditional expectations. The preference parameters are estimated in two stages. We estimate...