We study linear regressions in a context where the outcome of interest and some of the covariates are observed in two different datasets that cannot be matched. Traditional approaches obtain point identification by relying, often implicitly, on exclusion restrictions. We show that without such...

The following research aims to estimate the Gini coefficient, the Kernel distribution and the counterfactual decomposition of inequality in Bolivia. The methodology estimates the Gini coefficient, the kernel function, and the Shapley decomposition by using data from the 2022 Household Survey...

This paper introduces the Difference-in-Kinks (DiK) design, an econometric framework that extends the standard regression kink design to settings in which the slope of a policy rule varies over time. By combining the key features of the regression kink and difference-in-differences approaches,...

Missing covariate data is a prevalent problem in empirical research. We provide a novel framework for handling missing covariate data for estimation and inference in downstream tasks. Our general framework provides an automatic and easy-to-use pipeline for empirical researchers: First, missing...

Many instrumental variables applications specify a single Bernoulli treatment. But instruments may change outcomes through multiple pathways or by varying treatment intensity. Lottery instruments that boost charter school enrollment, for instance, may affect outcomes by lengthening time enrolled...

We introduce a framework for estimating demand across multiple assets with bidding data. Unlike existing methods, our approach does not rely on price instruments, which are often difficult to obtain. We describe the data requirements for implementation and illustrate its versatility using two...

Copulas and their uses in statistics, namely biostatistics, are a relatively new field of research. When modeling the dependence structure between a vector random variable's joint distribution and marginal distributions, copulas are crucial. In this article, we discuss recent research on this...

This paper provides nonparametric identification results for random coefficient distributions in perturbed utility models. We cover discrete choice and models of multiple purchases. We establish identification using variation in mean quantities. The results apply even when an analyst observes...

Objective:The article is devoted to the readiness of manufacturing enterprises to introduce the assumptions of the circular economy (CE). The purpose of the study is to see if there are statistically significant differences between the declared readiness to adopt the principles of the circular...

We propose a new approach to estimate selection-corrected quantiles of the gender wage gap. Our method employs instrumental variables that explain variation in the latent variable but, conditional on the latent process, do not directly affect selection. We provide semiparametric identification...