Working Papers

Finite-Sample Inference on Auction Bid Distributions Using Transaction Prices

2024, submitted
(with David M. Kaplan)

paper | code/tex/etc.

We provide finite-sample, nonparametric, uniform confidence bands for the underlying bid quantile function under symmetric IPV when only the transaction price is observed (i.e., only one order statistic). We make two extensions: a) varying number of bidders, b) auction-level unobserved heterogeneity (including new bounds).

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.

Dairy Trade Policy and Friction Analyses: (New) Gravity Estimations

2024, submitted
(with Magda Kondaridze and Jeff Luckstead)

We study the counterfactual distributional effect of dairy trade policy and friction variables using a recent distribution regression method (Chernozhukov, Fernández-Val, and Weidner, 2024). We revisit the mean effect using a recent PPML method (Weidner and Zylkin, 2021), which rigorously corrects the bias from incidental parameter problem.  

Publications

Averaging Estimation for Instrumental Variables Quantile Regression

2024, 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

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. As a separate contribution, I also provide the large sample theory for the constrained smoothed GMM estimator in the quantile model.

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

2024, 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.