Econometrics Papers of the Week

We explain why existing corrections for measurement error in RD are not well suited and propose a simple imputation-based solution.

We introduce the convolution skew-$t$ (CST) distribution, which combines independent standardized skew-$t$ subvectors through a general loading matrix to accommodate heterogeneous tails, directional asymmetry, and non-elliptical dependence.

We propose robust tests for causal effects on potentially high-dimensional outcomes where there may be selection into treatment.

Listwise deletion (LWD) appears more common than imputation: our review of leading journals finds that about 50% of articles state or imply LWD, versus 7% describing an imputation model.

In this paper, we develop a projected instrumental variable framework that relaxes conventional assumptions for causal identification with instrumental variables.

The contributions draw on a range of methods, from panel econometrics and volatility modeling to bibliometric analysis, altmetrics and systematic review.

Patch length is known to have a substantial impact on forecasting performance, yet its selection remains largely empirical. We develop a theoretical framework that explains why patch length matters for forecasting accuracy and provides a principled basis for its selection.

Specifically, we propose a two-step conditional EVaR estimator that achieves the parametric convergence rate.

We illustrate the method with PSID earnings data.

This paper examines robustness and causal interpretation of panel estimates using double/debiased machine learning.

We develop a score-driven volatility model in which no particular parametric innovation distribution needs to be specified.

Using data for 245 regions in 31 countries over 2016–2023, this study addresses three research questions with panel econometrics, cluster analysis, and machine learning.

We propose the Specification LASSO (S-LASSO), a two-step method that integrates LASSO and Adaptive Group LASSO to simultaneously perform variable selection and model specification.

In such cases, a targeted intervention that mitigates the externality at the congested option can divert it elsewhere and also undermine preference based sorting, making overall welfare effects theoretically ambiguous. We develop an empirical framework to analyze such scenarios of endogenous congestion.

We establish that the welfare distribution is the same functional of structural choice probabilities under stable but limited consideration as under full consideration.

This paper proposes and tests a synergy that assigns each tradition the task it is built for.

We introduce the Double Diagonal (DD) estimator, a novel method for interference-robust treatment effect estimation.

This paper characterizes what residuals identify about the error covariance.