We develop an estimator of unreported income, perhaps due to tax evasion, that does not depend on as strict identifying assumptions as previous estimators based on microeconomic data. The standard identifying assumption that the self-employed underreport income whereas wage and salary workers do...

This paper offers some new directions in the analysis of nonparamertric models with exogenous treatment assignment. The nonparametric approach opens the door to the examination of potentially different distributed outcomes. When combined with cross-validation, it also identifies potentially...

This paper proposes a quantile regression estimator for a panel data model with interactive effects potentially correlated with the independent variables. We provide conditions under which the slope parameter estimator is asymptotically Gaussian. Monte Carlo studies are carried out to...

This paper estimates a wage equation with three high-dimensional fixed effects, using a longitudinal matched employer-employee dataset covering virtually all Portuguese wage earners over a little more than two decades. The variation in log real hourly wages is decomposed into different...

The wage effect of job-education vertical mismatch (i.e. overeducation) has only recently been investigated in the case of Ph.D. holders. The existing contributions rely on OLS estimates that allow measuring the average effect of being mismatched at the mean of the conditional wages...

Researchers are often interested in estimating the causal effect of some treatment on individual criminality. For example, two recent relatively prominent papers have attempted to estimate the respective direct effects of marriage and gang participation on individual criminal activity. One...

Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Average Treatment Effects (LATE). When the running variable is observed with continuous measurement error, identification fails. Assuming non-differential measurement error, we propose a consistent...

In this article we overview nonparametric (spline and kernel) regression methods and illustrate how they may be used in labor economic applications. We focus our attention on issues commonly found in the labor literature such as how to account for endogeneity via instrumental variables in a...

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of...