Research methods

This chapter studies the microeconometric treatment-effect and structural approaches to dynamic policy evaluation.

In this paper we show that the sharp RDD straightforwardly generalizes to the instances in which the eligibility for the program is established with respect to an observable pre-program measure with eligible individuals self-selecting into the treatment group according to an unknown process.

This paper presents the econometric approach to causal modeling

We introduce test statistics based on generalized empirical likelihood methods that can be used to test simple hypotheses involving the unknown parameter vector in moment condition time series models.

In this paper, two methodological advancements are developed: First, this methodology permits to combine a data set on previously treated individuals with a data set on new clients when the regressors available in these two data sets do not coincide. It thereby incorporates additional regressors on previously treated that are not available for the current clients. Second, statistical inference on the recommended treatment choice is analyzed and conveyed to the agent, physician or case worker in a comprehensible and transparent way.

The matching method for treatment evaluation does not balance selective unobserved differences between treated and non-treated. We derive a simple correction term if there is an instrument that shifts the treatment probability to zero in specific cases.

This paper develops IV estimators for unconditional quantile treatment effects (QTE) when the treatment selection is endogenous.

This paper considers a linear triangular simultaneous equations model with conditional quantile restrictions.

I study inverse probability weighted M-estimation under a general missing data scheme.