Research
Working Papers
“Fixed-smoothing Uniform Inference for Quantile Regression”
with Antonio F. Galvao, Seunghwa Rho, Timothy J. Vogelsang, Jungmo Yoon
first circulation - 2026 - arXiv:2609.05883
arXiv
Abstract
This paper develops fixed-smoothing (fixed-b, fixed-K) inference methods for time-series quantile regression that are robust to heteroskedasticity and autocorrelation. Our approach is uniformly valid over quantile levels and accounts for dependence both over time and across quantiles. It enables the construction of uniform confidence bands, Wald, and Sup-t tests for joint hypotheses, and tests of shape restrictions, providing a unified framework for assessing heterogeneity in quantile effects. A key challenge is that, under weak dependence, uniform inference for quantile regression processes is generally non-pivotal because the limiting distributions depend on the long-run covariance structure across quantiles. To address this issue, we develop two complementary approaches. The uniform-in-τ method estimates the covariance structure and simulates the non-pivotal limiting distribution. For certain tests involving a finite collection of quantile levels, the stack-Wald method delivers pivotal fixed-smoothing inference. We establish the asymptotic validity of both approaches. Simulation results show that the proposed methods substantially improve size control relative to existing HAC-based procedures while maintaining good power. An application to predictive quantile regressions for stock returns reveals substantial heterogeneity in predictive effects across both quantiles and forecast horizons.“Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence”
with Harold D. Chiang
first circulation - 2026 - arXiv:2602.11333
arXiv
Abstract
This paper develops an asymptotic theory for two-step debiased machine learning (DML) estimators in generalised method of moments (GMM) models with general multiway clustered dependence, without relying on cross-fitting. While cross-fitting is commonly employed, it can be statistically inefficient and computationally burdensome when first-stage learners are complex and the effective sample size is governed by the number of independent clusters. We show that valid inference can be achieved without sample splitting by combining Neyman-orthogonal moment conditions with a localisation-based empirical process approach, allowing for an arbitrary number of clustering dimensions. The resulting debiased GMM estimators are shown to be asymptotically linear and asymptotically normal under multiway clustered dependence. A central technical contribution of the paper is the derivation of novel global and local maximal inequalities for empirical processes indexed by general classes of functions for separately exchangeable arrays, which underpin our theoretical arguments and are of independent interest.“Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity”
Best Student Paper Award at 2024 Midwest Econometrics Group Meeting
first circulation - 2025 - arXiv:2504.18772
arXiv latest version code & replication
Abstract
Panel data allows for the modeling of unobserved heterogeneity, significantly raising the number of nuisance parameters and making high dimensionality a practical issue. Meanwhile, temporal and cross-sectional dependence in panel data further complicates high-dimensional estimation and inference. This paper proposes a toolkit for high-dimensional panel models with large cross-sectional and time sample sizes. To reduce the dimensionality, I propose a variant of LASSO for two-way clustered panels. While being consistent, the convergence rate of LASSO is slow due to the cluster dependence, rendering inference challenging in general. Nevertheless, asymptotic normality can be established in a semiparametric moment-restriction model by leveraging a clustered-panel cross-fitting approach and, as a special case, in a partial linear model using the full sample. In an exercise of estimating multiplier using panel data, I demonstrate how high dimensionality could be hidden and the proposed toolkit enables flexible modeling and robust inference.“Identification of Average Responses with Endogenous Controls”
with Kyoo il Kim
first circulation - 2024 - arXiv:2401.14395
arXiv code & replication
Abstract
Control variables are routinely treated as exogenous, yet in many empirical settings they are themselves endogenous. This creates a dilemma: omitting controls may leave the treatment endogenous, while including them may contaminate identification. The problem is not resolved by instrumental variables when they are only conditionally valid. We show that average responses to the treatment remain identified under a rank condition called measurable separability, which accommodates endogenous controls. For parametric models, our approach amounts to estimating a nonparametric model that nests the parametric specification. For nonparametric models, our results imply that endogenous controls are generally innocuous under standard identification conditions, except in the presence of "bad controls". We further propose a test for endogenous controls. Simulation results and an empirical application demonstrate this prevalent issue and provide practical implications of our methods.“Another Look at the Linear Probability Model and Nonlinear Index Models”
with Robert S. Martin and Jeffrey M. Wooldridge (Revise & Resubmit at Econometric Reviews)
first circulation - 2023 - arXiv:2308.15338
arXiv latest version code & replication
Abstract
We reassess the linear probability model (LPM) for binary responses from the perspective of average partial effects (APEs). Under certain conditions, the LPM recovers APEs even when the true response probability is nonlinear. Outside these scenarios, we demonstrate in simulations that having a large fraction of fitted values in [0, 1] is neither necessary nor sufficient for OLS to approximate the APEs well, which contradicts the common rationale. We further investigate and refine an ad hoc trimming procedure to reduce bias for LPM, and establish its statistical properties by relating it to a numerically equivalent nonlinear least squares estimator.Publication
“Fixed-b Asymptotics for Panel Models with Two-Way Clustering”
with Timothy J. Vogelsang (Journal of Econometrics, 2024, 244(1): 105831)
first circulation - 2023 - arXiv:2309.08707
arXiv published version
STATA command: xtregtfb. Installation: type net from https://kaichengchen.github.io/statafile/ in STATA