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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

paper | code

I construct uniform confidence bands and bootstrap confidence interval for a quantile-based heterogeneous causal effects function in a nonadditive fixed effects model.

Averaging Estimation for Instrumental Variables Quantile Regression

2019/2023, submitted

paper | code | online appendix

I propose averaging methods to improve IVQR estimation efficiency.


Publications

Testing in smoothed GMM quantile models with an application to quantile Euler equation

Forthcoming, Econometrics and Statistics

published | accepted | code

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)

published | accepted | code

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.