Publications

• Measuring the Effects of Bid-rigging on Prices with Binary Misclassification (with Changsik Kim and Hyunchul Kim), Review of Industrial Organization (2022)

Abstract

The binary indicator of collusion is the key ingredient in estimating overcharges from bid-rigging with a regression-based approach. We develop a method for examining the effects of misclassification error in the indicator of bid-rigging status on estimates of damages from collusion. We derive partial identification of the regression model of winning bids in public procurement auctions and provide informative bounds on the price effects of bid-rigging. We find that the bounds are tight when placing a plausible restriction on the extent of measurement errors. Our findings show that relaxing the nondifferential assumption about misclassification errors leads to wider bounds.

Working papers

• Censored Quantile Regression with Many Controls

Abstract

This paper develops estimation and inference methods for censored quantile regression models with high-dimensional controls. The methods are based on the application of double/debiased machine learning (DML) framework to the censored quantile regression estimator of Buchinsky and Hahn (1998). I provide valid inference for low-dimensional parameters of interest in the presence of high-dimensional nuisance parameters when implementing machine learning estimators. The proposed estimator is shown to be consistent and asymptotically normal. The performance of the estimator with high-dimensional controls is illustrated with numerical simulation and an empirical application that examines the effect of 401(k) eligibility on savings.

Work in progress

• Distribution Regression with Censored Selection (with Ivan Fernandez-Val)