Jungmo Yoon
Hanyang University · Mathematics
About the Lab
Professor Jungmo Yoon's research lab specializes in econometric methods and empirical industrial organization, with a strong focus on developing robust statistical techniques for panel data and quantile regression. The lab investigates the performance and governance of business groups—particularly Korean chaebols—through the lens of productivity, technological capabilities, and investment efficiency, especially in the context of economic crises and institutional change. It also contributes to legal and institutional economics by analyzing the impact of litigation rules, such as the English rule, on settlement outcomes and legal incentives. The lab's work bridges theoretical econometrics with real-world policy and firm-level performance analysis.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15This article differentiates itself from the large volume of existing literature on business groups, such as Korean chaebols, in several aspects. First, it uses productive efficiency rather than financial efficiency as a performance measure. Second, it defines chaebols in three alternative ways and checks whether the results are robust. Third and most important, it explains the sources of the post‐crisis change in the performance of Korean chaebols in terms of technological capabilities and inves
The existence of the business groups has been associated with market failure in emerging economies, and thus their performance has been argued and found to have declined with development of market institutions surrounding them. This paper takes up this issue of long-term performance of the business groups but argues that it has also to do with the internal problems, such as changes in the ownership and governance structure. It finds, with the Korea data and new method and theoretical grounds, th
This study develops methods for conducting uniform inference on quantile treatment effects for sharp regression discontinuity designs. We develop a score test for the treatment significance hypothesis and Wald-type tests for the hypotheses related to treatment significance, homogeneity, and unambiguity. The bias from the nonparametric estimation is studied in detail. In particular, we show that under some conditions, the asymptotic distribution of the score test is unaffected by the bias, withou
This study develops cluster robust inference methods for panel quantile regression (QR) models with individual fixed effects, allowing for temporal correlation within each individual. The conventional QR standard errors can seriously underestimate the uncertainty of estimators and, therefore, overestimate the significance of effects, when outcomes are serially correlated. Thus, we propose a clustered covariance matrix (CCM) estimator to solve this problem. The CCM estimator is an extension of th
The English rule prescribes that the loser of a lawsuit pays the winner's litigation costs. Previous research on the English rule finds that plaintiffs win more often at trial, receive higher awards, and receive larger settlements. Theory predicts that the English rule discourages settlement by raising the threshold payment necessary for settlement. In this paper, we reexamine the Florida experiment with the English rule by placing bounds on the selection effects. We find that the mean and media
This study considers an estimator for the asymptotic variance-covariance matrix in time-series quantile regression models which is robust to the presence of heteroscedasticity and autocorrelation. When regression errors are serially correlated, the conventional quantile regression standard errors are invalid. The proposed solution is a quantile analogue of the Newey-West robust standard errors. We establish the asymptotic properties of the heteroscedasticity and autocorrelation consistent (HAC)
This paper presents estimation methods and asymptotic theory for the analysis of a nonparametrically specified conditional quantile process. Two estimators based on local linear regressions are proposed. The first estimator applies simple inequality constraints while the second uses rearrangement to maintain quantile monotonicity. The bandwidth parameter is allowed to vary across quantiles to adapt to data sparsity. For inference, the paper first establishes a uniform Bahadur representation and
Abstract Some linkages between kernel and penalty methods of density estimation are explored. It is recalled that classical Gaussian kernel density estimation can be viewed as the solution of the heat equation with initial condition given by data. We then observe that there is a direct relationship between the kernel method and a particular penalty method of density estimation. For this penalty method, solutions can be characterized as a weighted average of Gaussian kernel density estimates, the
Replication Data for: Estimating the Effects of the English Rule on Litigation Outcomes
The English rule for fee allocation prescribes that the loser of a lawsuit pay the winner’s litigation costs. Economic theory predicts that the English rule discourages settlement, increases litigation costs and encourages meritorious claims. The principal empirical work on the impact of the English rule by Hughes and Snyder (1990, 1995) relies on data from Florida’s use of the Rule for medical malpractice claims between 1980 and 1985. The principal findings are that plaintiffs win more often at
Research Areas
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