352 citations · 680 across the 21 of their papers we have counts for
21 papers
A Stochastic Nested Fixed Point Algorithm for Large-Scale BLP Estimation
Zhentong Lu, Myung Hwan Seo, Youngki Shin +1
We develop a stochastic nested fixed point (SNFP) estimator for random coefficients logit demand models that updates model parameters using stochastic gradients and performs demand…
SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models
Sokbae Lee, Yuan Liao, Myung Hwan Seo +1
Multinomial choice models allow flexible substitution patterns but become computationally demanding with many alternatives or observations. With a fixed per-observation simulation…
SGMM: Stochastic Approximation to Generalized Method of Moments
Xiaohong Chen, Sokbae Lee, Yuan Liao +3
We introduce a new class of algorithms, Stochastic Generalized Method of Moments (SGMM), for estimation and inference on (overidentified) moment restriction models. Our SGMM is a n…
Bootstraps for Dynamic Panel Threshold Models
Woosik Gong, Myung Hwan Seo
This paper develops valid bootstrap inference methods for the dynamic short panel threshold regression. We show that the standard nonparametric bootstrap is inconsistent for the fi…
Fast Inference for Quantile Regression with Tens of Millions of Observations
Sokbae Lee, Yuan Liao, Myung Hwan Seo +1
Big data analytics has opened new avenues in economic research, but the challenge of analyzing datasets with tens of millions of observations is substantial. Conventional econometr…
What Impulse Response Do Instrumental Variables Identify?
Bonsoo Koo, Seojeong Lee, Myung Hwan Seo +1
We study what local projection instrumental variable (LP-IV) regressions identify when an instrument is correlated with several structural shocks, such as defense and non-defense s…
Minimax Risk in Estimating Kink Threshold and Testing Continuity
Javier Hidalgo, Heejun Lee, Jungyoon Lee +1
We derive a risk lower bound in estimating the threshold parameter without knowing whether the threshold regression model is continuous or not. The bound goes to zero as the sample…
Regression Discontinuity Design with Potentially Many Covariates
Yoichi Arai, Taisuke Otsu, Myung Hwan Seo
This paper studies the case of possibly high-dimensional covariates in the regression discontinuity design (RDD) analysis. In particular, we propose estimation and inference method…
Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling
Sokbae Lee, Yuan Liao, Myung Hwan Seo +1
We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms. We lever…
Frequent or Systematic Changes? discussion on "Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection."
Myung Hwan Seo
We discuss Fryzlewicz's (2020) that proposes WBS2.SDLL approach to detect possibly frequent changes in mean of a series. Our focus is on the potential issues related to the model m…
Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic
Xiaohong Chen, Sokbae Lee, Myung Hwan Seo +1
Many economic panel and dynamic models, such as rational behavior and Euler equations, imply that the parameters of interest are identified by conditional moment restrictions. We i…
Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate
Sokbae Lee, Yuan Liao, Myung Hwan Seo +1
In this paper, we estimate the time-varying COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model. Our measurement of the contact rate is constructed using data on…
Robust Inference on Infinite and Growing Dimensional Time Series Regression
Abhimanyu Gupta, Myung Hwan Seo
We develop a class of tests for time series models such as multiple regression with growing dimension, infinite-order autoregression and nonparametric sieve regression. Examples in…
Desperate times call for desperate measures: government spending multipliers in hard times
Sokbae Lee, Yuan Liao, Myung Hwan Seo +1
We investigate state-dependent effects of fiscal multipliers and allow for endogenous sample splitting to determine whether the US economy is in a slack state. When the endogenized…
Estimation of Dynamic Panel Threshold Model using Stata
Myung Hwan Seo, Sueyoul Kim, Young-Joo Kim
We develop a Stata command xthenreg to implement the first-differenced GMM estimation of the dynamic panel threshold model, which Seo and Shin (2016, Journal of Econometrics 195: 1…
Factor-Driven Two-Regime Regression
Sokbae Lee, Yuan Liao, Myung Hwan Seo +1
We propose a novel two-regime regression model where regime switching is driven by a vector of possibly unobservable factors. When the factors are latent, we estimate them by the p…
Robust inference for threshold regression models
Javier Hidalgo, Jungyoon Lee, Myung Hwan Seo
This paper is concerned with inference in threshold regression models when the practitioners do not know whether at the threshold point the true specification has a kink or a jump.…
Local M-estimation with Discontinuous Criterion for Dependent and Limited Observations
Myung Hwan Seo, Taisuke Otsu
This paper examines asymptotic properties of local M-estimators under three sets of high-level conditions. These conditions are sufficiently general to cover the minimum volume pre…
Oracle Estimation of a Change Point in High Dimensional Quantile Regression
Sokbae Lee, Yuan Liao, Myung Hwan Seo +1
In this paper, we consider a high-dimensional quantile regression model where the sparsity structure may differ between two sub-populations. We develop -penalized estimator…
Structural Change in Sparsity
Sokbae Lee, Yuan Liao, Myung Hwan Seo +1
In the high-dimensional sparse modeling literature, it has been crucially assumed that the sparsity structure of the model is homogeneous over the entire population. That is, the i…
The Lasso for High-Dimensional Regression with a Possible Change-Point
Sokbae Lee, Myung Hwan Seo, Youngki Shin
We consider a high-dimensional regression model with a possible change-point due to a covariate threshold and develop the Lasso estimator of regression coefficients as well as the…