ORCID

0000-0003-2779-7796

Date of Award

Fall 2025

Language

English

Embargo Period

11-5-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Economics

Program

Economics

First Advisor

Ulrich Hounyo

Second Advisor

Kajal Lahiri

Third Advisor

Michael Jerison

Committee Members

Ulrich Hounyo, Kajal Lahiri, Michael Jerison

Keywords

bootstrap, jackknife, linear regression

Subject Categories

Econometrics | Economics

Abstract

The first chapter studies wild bootstrap-based inference for regression models with multiway clustering. Our proposed methods are multiway counterparts to the (one-way) wild cluster bootstrap approach introduced by Cameron et al. (2008). We establish the validity of our methods for studentized statistics. Theoretical results are provided, accommodating arbitrary serial dependence in the common time effects – an aspect excluded by existing two-way bootstrap-based approaches. Simulation experiments document the potential for enhanced inference with our novel approaches. We illustrate the effectiveness of the methods by revisiting an empirical study involving multiway clustered and serially correlated data.

The second chapter studies jackknife cluster-robust variance estimators (CRVEs) on multiway clustering. Chiang, Hansen, and Sasaki (2024) and Chen and Vogelsang (2024) developed CRVEs for handling arbitrary serial dependence in linear regressions with two-way clustered panel data. However, conventional CRVEs often perform poorly in finite samples. We propose several jackknife CRVEs and demonstrate their theoretical validity. Through extensive simulations, we show that certain jackknife CRVEs deliver remarkably improved inferences. This enhanced performance holds even in the presence of two-way fixed effects. Notably, one of our new approaches significantly mitigates issues of undefined standard errors when CRVEs are not positive definite, ensuring robust and consistent inference across scenarios.

The third chapter introduces a projection-based wild bootstrap (PWB) method for inference in linear regression models with two-way clustered data. We examine all possible scenarios for the asymptotic distribution of the estimator—Gaussian and non-Gaussian—classifying them into five distinct cases. In one scenario, no procedure can achieve uniform consistency under a fully unspecified DGP; to the best of our knowledge, our method is the first to cover the remaining four. We identify and apply two diagnostic factors to distinguish between these scenarios. In addition, our procedure accommodates arbitrary serial dependence. Simulation results demonstrate the accuracy and flexibility of the proposed method, making it a robust tool for empirical work involving complex clustering structures.

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This work is licensed under the University at Albany Standard Author Agreement.

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