Alejo, Javier

In this article, we present a new command, qcte, that implements several methods for estimation and inference for quantile treatment-effects models with a continuous treatment. We propose a semiparametric two-step estimator, where the first step is based on a flexible Box–Cox model, as the default model of the command. We develop practical statistical inference procedures using bootstrap. We implement some simulations to show that the proposed methods perform well. Finally, we apply qcte to a survey of Massachusetts lottery winners to estimate the unconditional quantile effects of the prize amount, as a proxy of nonlabor income changes, on subsequent labor earnings from U.S. Social Security records. The empirical results reveal strong heterogeneity across unconditional quantiles.

his working paper explores the effect of joint labor decisions on the study of wage regression models. The estimation of Mincer equations suffers from numerous sources of bias, including the sample selection problem generated by the fact that the agent decision to work is not independent of the wage. Most of the papers corrects this bias using a model of individual labor participation. However, recent trends in the labor market show greater participation of women in the labor force and seem to indicate that the joint decision of the spouses is increasingly relevant in determining the selection mechanism.

Continuous treatments (e.g., doses) arise often in practice. Methods for estimation and inference for quantile treatment effects models with a continuous treatment are proposed. Identification of the parameters of interest, the dose-response functions and the quantile treatment effects, is achieved under the assumption that selection to treatment is based on observable characteristics. An easy to implement semiparametric two-step estimator, where the first step is based on a flexible Box–Cox model is proposed. Uniform consistency and weak convergence of this estimator are established. Practical statistical inference procedures are developed using bootstrap...

This paper proposes a simple hierarchical model and a testing strategy to identify intra-cluster correlations, in the form of nested random effects and serially correlated error components. We focus on intra-cluster serial correlation at different nested levels, a topic that has not been studied in the literature before. A Neyman’s framework is used to derive LM-type tests that allow researchers to identify the appropriate level of clustering as well as the type of intra-group correlation. An extensive Monte Carlo exercise shows that the proposed tests perform well in finite samples and under non-Gaussian distributions.

This paper develops generalized method of moments-based (GMM-based) Lagrange multiplier tests for nonlinear hypotheses that are robust to locally misspecified possibly nonlinear alternatives. The procedure is based on an initial consistent GMM estimator of the parameters under a given set of nonlinear restrictions. The new test for one particular set of nonlinear hypotheses is consistent and has correct asymptotic size independently of whether the other, also nonlinear hypotheses, are correct or locally misspecified. To illustrate the usefulness of our proposed tests we consider testing rational expectations hypotheses using U.S. data.