Recentered influence functions (RIFs) are statistical tools popularized by Firpo, Fortin, and Lemieux (2009) for analyzing unconditional partial effects on quantiles in a regression analysis framework (unconditional quantile regressions). The flexibility and simplicity of these tools has opened...

Financial analysts assume that the reliability of predictions derived from regression analysis improves with sample size. This is generally true because larger samples tend to produce less noisy results than smaller samples. But this is not always the case. Some observations are more relevant...

The asymptotic distribution of the linear instrumental variables (IV) estimator with empirically selected ridge regression penalty is characterized. The regularization tuning parameter is selected by splitting the observed data into training and test samples and becomes an estimated parameter...

Centralized school assignment algorithms must distinguish between applicants with the same preferences and priorities. This is done with randomly assigned lottery numbers, nonlottery tie-breakers like test scores, or both. The New York City public high school match illustrates the latter, using...

This paper analyzes several modifications to improve a simple measure of vulnerability as expected poverty. Firstly, in order to model income, we apply distributional regression relating potentially each parameter of the conditional income distribution to the covariates. Secondly, we determine...

For numerous applications it is of interest to provide full probabilistic forecasts, which are able to assign probabilities to each predicted outcome. Therefore, attention is shifting constantly from conditional mean models to probabilistic distributional models capturing location, scale, shape...

In this chapter, we present econometric and statistical methods for analyzing randomized experiments. For basic experiments, we stress randomization-based inference as opposed to sampling-based inference. In randomization-based inference, uncertainty in estimates arises naturally from the random...

This paper characterizes the impact of serial dependence on the non-asymptotic estimation error bound of penalized regressions (PRs). Focusing on the direct relationship between the degree of cross-correlation of covariates and the estimation error bound of PRs, we show that orthogonal or weakly...

In this paper, we propose a new comprehensive framework for analysing wage discrimination. This framework assesses wage discrimination on the grounds of conditional wage distributions (rather than just conditional means), regards the whole population (rather than just those in work) and employs...

In this paper, we propose a new comprehensive framework for analysing wage discrimination. This framework assesses wage discrimination on the grounds of conditional wage distributions (rather than just conditional means), regards the whole population (rather than just those in work) and employs...