This paper investigates four topics. (1) It examines the different roles played by the propensity score (probabilitiy of selection) in matching, instrumental variable and control functions methods. (2) It contrasts the roles of exclusion restrictions in matching and selection models. (3) It...

This paper describes sfcross and sfpanel, two new Stata commands for the estimation of cross-sectional and panel data stochastic frontier models. sfcross extends the offi cial frontier capabilities by including additional models (Greene 2003; Wang 2002) and command functionality, such as the...

xsmle is a new command for spatial analysis using Stata. We consider the quasi-maximum likelihood estimation of a wide set of both fi xed- and random- eff ects spatial models for balanced panel data. Of special note is that xsmle allows to handle unbalanced panels thanks to its full...

This article describes qregsel, a Stata module to implement a copula-based sample selection correction for quantile regression recently proposed by Arellano and Bonhomme (2017, Econometrica 85(1): 1-28). The command allows the user to model selection in quantile regressions using either a...

In non-linear regression models, such as the probit model, coefficients cannot be interpreted as marginal effects. The marginal effects are usually non-linear combinations of all regressors and regression coefficients of the model. This paper derives the marginal effects in a probit model with a...

When considering multiple hypothesis tests simultaneously, standard statistical techniques will lead to over-rejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this paper we discuss the Romano-Wolf multiple hypothesis correction, and...

The paper presents a modification of the matching and difference-in-differences approach of Heckman et al. (1998) for the staggered treatment adoption design and a Stata tool that implements the approach. This flexible conditional difference-in-differences approach is particularly useful for...

In regression analysis, the presence of outliers in the data set can strongly distort the classical least squares estimator and lead to unreliable results. To deal with this, several robust-to-outliers methods have been proposed in the statistical literature. In Stata, some of these methods are...