Christian Hansen

We consider estimation of policy relevant treatment effects in a data-rich environment where there may be many more control variables available than there are observations.

In this article, we review quantile models with endogeneity.

We propose robust methods for inference on the effect of a treatment variable on a scalar outcome in the presence of very many controls.

In this article, we review quantile models with endogeneity.

We develop results for the use of Lasso and post-Lasso methods to form first-stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p.

The authors develop results for the use of LASSO and Post-LASSO methods to form first-stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p, that apply even when p is much larger than the sample size, n.

This paper explores efficiency gains which might be achievable using moment conditions which are nonlinear in the disturbances and are based on flexible parametric families for error distributions.

We give corrected standard errors, an extension of Bekker (1994) to nonnormal disturbances, that adjust for many instruments.

Using many valid instrumental variables has the potential to improve efficiency but makes the usual inference procedures inaccurate.