Research methods

We propose a new specification test for assessing the validity of fuzzy regression discontinuity designs (FRD-validity).

This paper first reviews some of the approaches that have been taken to estimate the common parameters of binary outcome models with fixed effects.

We study the incidental parameter problem for the “three-way” Poisson Pseudo-Maximum Likelihood (PPML) estimator recently recommended for identifying the effects of trade policies and in other panel data gravity settings.

We show the importance of Berkson errors for demand estimation with nonseparable unobserved heterogeneity.

We study an extension of a treatment effect model in which an observed discrete classifier indicates which one of a set of counterfactual processes occurs, each of which may result in the realization of several endogenous outcomes.

We study testable implications of multiple equilibria in discrete games with incomplete information. Unlike de Paula and Tang (2012), we allow the players’ private signals to be correlated.

We provide estimation methods for panel nonseparable models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data.

This article estimates the importance of temptation for consumption smoothing and asset accumulation in a life‐cycle model.

Many empirical questions concern target parameters selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data.

Recent contributions using police recorded calls-for-service and/or crime data to estimate impacts of COVID-19 lockdowns on the incidence of domestic violence (DV) have reported relatively modest effects.

This paper analyzes Structural Vector Autoregressions (SVARs) where identification of structural parameters holds locally but not globally.

Researchers are often interested in the relationship between two variables, with no single data set containing both.

We propose a new simulation-based estimation method, adversarial estimation, for structural models.

We propose a framework for estimation and inference when the model may be misspecified.

This paper builds on Bonhomme (2012) to develop a method to systematically construct moment conditions for dynamic panel data logit models with fixed effects.

In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector.

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice.

This paper is concerned with learning decision makers’ preferences using data on observed choices from a finite set of risky alternatives.

We consider both l0-penalized and l0-constrained quantile regression estimators.