arXiv subjects

Kaicheng Chen

Publications and source records attributed to Kaicheng Chen.

6 recordsLinked to original sources

arXiv2026

Fixed-smoothing Uniform Inference for Quantile Regression

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.

econ.EM↗

arXiv2026

Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence

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 orthogonal moments 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. Python and R packages are available for implementing the proposed procedures.

econ.EM↗

arXiv2026

Identification of Average Responses with Endogenous Controls

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.

econ.EM↗

arXiv2025

Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity

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.

econ.EM↗

arXiv2024

Fixed-b Asymptotics for Panel Models with Two-Way Clustering

This paper studies a cluster robust variance estimator proposed by Chiang, Hansen and Sasaki (2024) for linear panels. First, we show algebraically that this variance estimator (CHS estimator, hereafter) is a linear combination of three common variance estimators: the one-way unit cluster estimator, the "HAC of averages" estimator, and the "average of HACs" estimator. Based on this finding, we obtain a fixed-$b$ asymptotic result for the CHS estimator and corresponding test statistics as the cross-section and time sample sizes jointly go to infinity. Furthermore, we propose two simple bias-corrected versions of the variance estimator and derive the fixed-$b$ limits. In a simulation study, we find that the two bias-corrected variance estimators along with fixed-$b$ critical values provide improvements in finite sample coverage probabilities. We illustrate the impact of bias-correction and use of the fixed-$b$ critical values on inference in an empirical example on the relationship between industry profitability and market concentration.

econ.EM↗

arXiv2023

Another Look at the Linear Probability Model and Nonlinear Index Models

We reassess the use of linear models to approximate response probabilities of binary outcomes, focusing on average partial effects (APE). We confirm that linear projection parameters coincide with APEs in certain scenarios. Through simulations, we identify other cases where OLS does or does not approximate APEs and find that having large fraction of fitted values in [0, 1] is neither necessary nor sufficient. We also show nonlinear least squares estimation of the ramp model is consistent and asymptotically normal and is equivalent to using OLS on an iteratively trimmed sample to reduce bias. Our findings offer practical guidance for empirical research.

econ.EM↗