378 citations · 954 across the 33 of their papers we have counts for
35 papers
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…
Fixed-order PCA: Theory for Overestimated Factor Models
Yuan Liao, Xin Tong, Wanjie Wang +1
We develop asymptotic theory for principal component analysis (PCA) of a high-dimensional factor model in which the working dimension is fixed and only required to satisfy $R \…
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…
Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation
Jungjun Choi, Hyukjun Kwon, Yuan Liao
This paper studies the inferential theory for estimating low-rank matrices. It also provides an inference method for the average treatment effect as an application. We show that th…
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves
Xiaohong Chen, Yuan Liao, Weichen Wang
General nonlinear sieve learnings are classes of nonlinear sieves that can approximate nonlinear functions of high dimensional variables much more flexibly than various linear siev…
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…
Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes
Ivan Fernandez-Val, Wayne Yuan Gao, Yuan Liao +1
We introduce a dynamic distribution regression panel data model with heterogeneous coefficients across units. The objects of primary interest are functionals of these coefficients,…
Inference for Low-Rank Models
Victor Chernozhukov, Christian Hansen, Yuan Liao +1
This paper studies inference in linear models with a high-dimensional parameter matrix that can be well-approximated by a ``spiked low-rank matrix.'' A spiked low-rank matrix has r…
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…
Recent Developments on Factor Models and its Applications in Econometric Learning
Jianqing Fan, Kunpeng Li, Yuan Liao
This paper makes a selective survey on the recent development of the factor model and its application on statistical learnings. We focus on the perspective of the low-rank structur…
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…
Feasible Generalized Least Squares for Panel Data with Cross-sectional and Serial Correlations
Jushan Bai, Sung Hoon Choi, Yuan Liao
This paper considers generalized least squares (GLS) estimation for linear panel data models. By estimating the large error covariance matrix consistently, the proposed feasible GL…
Standard Errors for Panel Data Models with Unknown Clusters
Jushan Bai, Sung Hoon Choi, Yuan Liao
This paper develops a new standard-error estimator for linear panel data models. The proposed estimator is robust to heteroskedasticity, serial correlation, and cross-sectional cor…
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…
Learning Latent Factors from Diversified Projections and its Applications to Over-Estimated and Weak Factors
Jianqing Fan, Yuan Liao
Estimations and applications of factor models often rely on the crucial condition that the number of latent factors is consistently estimated, which in turn also requires that fact…
Inference for Heterogeneous Effects using Low-Rank Estimation of Factor Slopes
Victor Chernozhukov, Christian Hansen, Yuan Liao +1
We study a panel data model with general heterogeneous effects where slopes are allowed to vary across both individuals and over time. The key dimension reduction assumption we emp…
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…
Uniform Inference for Characteristic Effects of Large Continuous-Time Linear Models
Yuan Liao, Xiye Yang
We consider continuous-time models with a large panel of moment conditions, where the structural parameter depends on a set of characteristics, whose effects are of interest. The l…
The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications
Christian Hansen, Yuan Liao
We consider inference about coefficients on a small number of variables of interest in a linear panel data model with additive unobserved individual and time specific effects and a…
A Note on Choosing the Threshold for Large Covariance Estimations in Factor Models
Yuan Liao
This note shows that for i.i.d. data, estimating large covariance matrices in factor models can be casted using a simple plug-in method to choose the threshold: $$ μ_{jl}=\frac{c_0…
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…
An Overview on the Estimation of Large Covariance and Precision Matrices
Jianqing Fan, Yuan Liao, Han Liu
Estimating large covariance and precision matrices are fundamental in modern multivariate analysis. The problems arise from statistical analysis of large panel economics and financ…
A lava attack on the recovery of sums of dense and sparse signals
Victor Chernozhukov, Christian Hansen, Yuan Liao
Common high-dimensional methods for prediction rely on having either a sparse signal model, a model in which most parameters are zero and there are a small number of non-zero param…
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…