111 citations · 262 across the 16 of their papers we have counts for
17 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
Simulated maximum likelihood is widely used in latent-variable models where likelihood evaluation requires intractable integrals or probabilities. With a fixed number of 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…
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…
Statistical Treatment Rules under Social Interaction
Seungjin Han, Julius Owusu, Youngki Shin
In this paper we study treatment assignment rules in the presence of social interaction. We construct an analytical framework under the anonymous interaction assumption, where the…
csa2sls: A complete subset approach for many instruments using Stata
Seojeong Lee, Siha Lee, Julius Owusu +1
We develop a Stata command that implements the complete subset averaging two-stage least squares (CSA2SLS) estimator in Lee and Shin (2021). The CSA2SLS estimato…
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…
Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach
Rui Fan, Ji Hyung Lee, Youngki Shin
In this paper we propose the adaptive lasso for predictive quantile regression (ALQR). Reflecting empirical findings, we allow predictors to have various degrees of persistence and…
Exact Computation of Maximum Rank Correlation Estimator
Youngki Shin, Zvezdomir Todorov
In this paper we provide a computation algorithm to get a global solution for the maximum rank correlation estimator using the mixed integer programming (MIP) approach. We construc…
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…
Complete Subset Averaging for Quantile Regressions
Ji Hyung Lee, Youngki Shin
We propose a novel conditional quantile prediction method based on complete subset averaging (CSA) for quantile regressions. All models under consideration are potentially misspeci…
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…
Complete Subset Averaging with Many Instruments
Seojeong Lee, Youngki Shin
We propose a two-stage least squares (2SLS) estimator whose first stage is the equal-weighted average over a complete subset with instruments among available, which we call…
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…
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…