Welcome, I am Jeonghwan Kim and a job market candidate in 2020 - 2021.
My research interest is econometric theory and applied econometrics. 

Jeonghwan Kim

Ph.D. Candidate in Economics






"Semi-Parametric Model under Monotonicity" (Job Market Paper) 

This paper studies estimation and inference of a semi-parametric model under monotone shape restriction on non-parametric part. I develop a new semi-parametric estimator that can be derived without choosing any smoothing parameters and construct the confidence band of semi-nonparametric parameters under monotonicity. In economic application, I estimate the returns to schooling with using the conditional expectation of 'Knowledge of the World of Work' score given age as a monotone non-parametric instrumental variable (NPIV). The confidence interval of the returns to schooling and the confidence band of the age effect on the log of wage under assuming monotone relationship are conducted. In Monte Carlo simulation, the illustration of the confidence interval of semi-parametric estimator and the confidence band of the semi-nonparametric estimator are attached.

"A Small Sigma Approach to Certain Problems in Errors-in-Variables and Panel Data
(with Jinyong Hahn and Jerry Hausman)

Submitted to Econometrica Draft

We propose a pragmatic approach to the errors-in-variables and nonlinear panel models. These
models are often deemed impossible to estimate in their most general forms. For example,
the higher order moments approach to errors-in-variables model fails when there is conditional
heteroscedasticity. Similarly, nonlinear panel models with fixed effects and small T are known to
be problematic to estimate. We propose estimating these models using approximate moments,
using a Taylor series approximation applied to Kadane’s (1971) small sigma approach. Simulation
results suggest that the approximation leads to reasonable sampling properties. Our proposal
complements the newly resurgent literature on sensitivity analysis.



“Public transfer income, retirement, living alone and the consumption behavior of older house-holds: Evidence from Korea using Panel Quantile regression,” (with Yeonha Jung) Yonsei Graduate Student Association Vol.58, 271-292. (2013) Draft

In this paper, we investigate the consumption of older households focusing on the role of public transfer income, retirement, and living alone using KLIPS (Korean Labor and Income Panel Study) data over the period 2005 to 2010. Considering that the heterogeneity among older households has been on the rise, we adopt Panel Quantile regression method. Panel Quantile regression analysis allows us to analyze the effects of explanatory variables on the consumption of older households according to each consumption class. We show that first, an increase of public transfer income enhances consumption significantly only in very low consumption classes. This seems to be due to the differences in the dependency on public transfer income depending on consumption class. Second,  retirement reduces the consumption of older household significantly in all the classes and the decline is larger in lower consumption classes. Finally, living alone is also a factor reducing the consumption of older households significantly in all the classes but its effect is stronger in higher consumption classes.  



2019      Introduction to Econometrics

Graduate/Undergraduate Teaching Assistant

2020      Probability and Statistics for Economists

2019      Introduction to Econometrics, Probability and Statistics for Economists

2018      Introduction to Econometrics, Probability and Statistics for Economists

2017      Introduction to Econometrics, Probability and Statistics for Economists 

2016      Probability and Statistics for Economists 

Teaching Evaluations (Overall Average 7.47/9.00)


Introduction to Econometrics

     Spring 2019 (1, 2)

Teaching Assistant 

Probability and Statistics for Economists

     Fall 2016 (1, 2)     Winter 2017 (1, 2)     Fall 2018 (1, 2)     Fall 2019 (1, 2)     Winter 2019 (1, 2)   

     Winter 2020 (1, 2)     Spring 2020 (1, 2)   

Introduction to Econometrics (Lab)

    Spring 2017 (1, 2)     Fall 2017 (1, 2)     Winter 2018 (12)     Spring 2019 (1, 2)     Spring 2020 (1

*1 and 2 are different sessions.