Xin Liu
Assistant Professor, Economics
Washington State University
xin.liu1@wsu.edu
Office: Hulbert Hall
Google Scholar profile
Curriculum Vitae
Fields of Interests:
Econometric Theory, Applied Econometrics, Quantile Regression, Panel Data
Working Papers
A quantile-based nonadditive fixed effects model
2020/2024, submitted
paper | code | online appendix
I propose a quantile-based nonadditive fixed effects model to study heterogeneous causal effects, while allowing endogeneity. This model assumes a more general functional form than the standard fixed effects model and complements certain fixed effects quantile regression model (Canay 2011).
Inference for Panel Quantile Regression with Time-Invariant Rank
2022, submitted
I construct uniform confidence bands and bootstrap confidence interval for a quantile-based heterogeneous causal effects function in a nonadditive fixed effects model.
Publications
Averaging Estimation for Instrumental Variables Quantile Regression
Forthcoming, Oxford Bulletin of Economics and Statistics
published | accepted | code | online appendix
I propose averaging methods to improve IVQR estimation efficiency.
Testing in smoothed GMM quantile models with an application to quantile Euler equation
Forthcoming, Econometrics and Statistics
I propose testing methods in smoothed GMM quantile model (de Castro, Galvao, Kaplan, and Liu, 2019), with quantile Euler equation empirical example.
Confidence Intervals for Intentionally Biased Estimators
2024, Econometric Reviews
(with David M. Kaplan)
We propose simple CIs using estimators that are intentionally biased to reduce MSE (like sivqr/SEE-IVQR). At 95% confidence level, these CIs improve the length and coverage probability compared to the benchmark CI using unbiased estimator.
k-Class Instrumental Variables Quantile Regression
Forthcoming, Empirical Economics
(with David M. Kaplan)
published | accepted | code/tex/etc.
We apply k-class estimation to IVQR to reliably reduce median bias for certain choices of k.
Smoothed GMM for quantile models
2019, Journal of Econometrics
(with Luciano de Castro, Antonio Galvao, and David M. Kaplan)
published | accepted | code/tex/etc.
We extend smoothed IVQR estimation (Kaplan and Sun, 2017) to non-iid data, nonlinear and over-identified models, with a quantile Euler equation empirical example.