In a randomized control trial, the precision of an average treatment effect estimator can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. We propose the use of pre-experimental data such as a census, or a...

The aim of this paper is to introduce a practical nonlinear generalization of PCA that captures nonlinear forms of dependence and delivers truly independent factors. The output of the method is a low-dimensional curvilinear coordinate system that tracks the important features of the data. The...

This paper proposes tests for instrument validity in sample selection models with non-randomly censored outcomes. Such models commonly invoke an exclusion restriction (i.e., the availability of an instrument affecting selection, but not the outcome) and additive separability of the errors in the...

Most sample selection models assume that the errors are independent of the regressors. Under this assumption, all quantile and mean functions are parallel, which implies that quantile estimators cannot reveal any (per definition non-existing) heterogeneity. However, quantile estimators are...

In a randomized control trial, the precision of an average treatment effect estimator can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. We propose the use of pre-experimental data such as a census, or a...

In a randomized control trial, the precision of an average treatment effect estimator can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. We propose the use of pre-experimental data such as a census, or a...