378 citations · 954 across the 33 of their papers we have counts for
12 papers · 1 filter
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…
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…
Projected principal component analysis in factor models
Jianqing Fan, Yuan Liao, Weichen Wang
This paper introduces a Projected Principal Component Analysis (Projected-PCA), which employs principal component analysis to the projected (smoothed) data matrix onto a given line…
Power Enhancement in High Dimensional Cross-Sectional Tests
Jianqing Fan, Yuan Liao, Jiawei Yao
We propose a novel technique to boost the power of testing a high-dimensional vector $H:\btheta=0$ against sparse alternatives where the null hypothesis is violated only by a coupl…
Semi-parametric Bayesian Partially Identified Models based on Support Function
Yuan Liao, Anna Simoni
We provide a comprehensive semi-parametric study of Bayesian partially identified econometric models. While the existing literature on Bayesian partial identification has mostly fo…
Efficient Estimation of Approximate Factor Models via Regularized Maximum Likelihood
Jushan Bai, Yuan Liao
We study the estimation of a high dimensional approximate factor model in the presence of both cross sectional dependence and heteroskedasticity. The classical method of principal…
High-dimensional covariance matrix estimation in approximate factor models
Jianqing Fan, Yuan Liao, Martina Mincheva
The variance--covariance matrix plays a central role in the inferential theories of high-dimensional factor models in finance and economics. Popular regularization methods of direc…