120 citations · 192 across the 12 of their papers we have counts for
13 papers
Growing the Efficient Frontier on Panel Trees
Lin William Cong, Guanhao Feng, Jingyu He +1
We introduce a new class of tree-based models, P-Trees, for analyzing (unbalanced) panel of individual asset returns, generalizing high-dimensional sorting with economic guidance a…
Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching
Siyu Bie, Francis X. Diebold, Jingyu He +1
We explore tree-based macroeconomic regime-switching in the context of the dynamic Nelson-Siegel (DNS) yield-curve model. In particular, we customize the tree-growing algorithm to…
Stochastic Tree Ensembles for Estimating Heterogeneous Effects
Nikolay Krantsevich, Jingyu He, P. Richard Hahn
Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for caus…
Local Gaussian process extrapolation for BART models with applications to causal inference
Meijiang Wang, Jingyu He, P. Richard Hahn
Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard im…
Bayesian Inference for Gamma Models
Jingyu He, Nicholas Polson, Jianeng Xu
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions. Our methodology applies to many situations in stati…
Stochastic tree ensembles for regularized nonlinear regression
Jingyu He, P. Richard Hahn
This paper develops a novel stochastic tree ensemble method for nonlinear regression, which we refer to as XBART, short for Accelerated Bayesian Additive Regression Trees. By combi…
Data Augementation with Polya Inverse Gamma
Jingyu He, Nicholas G. Polson, Jianeng Xu
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions. Our methodology applies to many situations in stati…
Factor Investing: A Bayesian Hierarchical Approach
Guanhao Feng, Jingyu He
This paper investigates asset allocation problems when returns are predictable. We introduce a market-timing Bayesian hierarchical (BH) approach that adopts heterogeneous time-vary…
XBART: Accelerated Bayesian Additive Regression Trees
Jingyu He, Saar Yalov, P. Richard Hahn
Bayesian additive regression trees (BART) (Chipman et. al., 2010) is a powerful predictive model that often outperforms alternative models at out-of-sample prediction. BART is espe…
Efficient sampling for Gaussian linear regression with arbitrary priors
P. Richard Hahn, Jingyu He, Hedibert Lopes
This paper develops a slice sampler for Bayesian linear regression models with arbitrary priors. The new sampler has two advantages over current approaches. One, it is faster than…
Deep Learning in Characteristics-Sorted Factor Models
Guanhao Feng, Jingyu He, Nicholas G. Polson +1
This paper presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for co…
Deep Learning for Predicting Asset Returns
Guanhao Feng, Jingyu He, Nicholas G. Polson
Deep learning searches for nonlinear factors for predicting asset returns. Predictability is achieved via multiple layers of composite factors as opposed to additive ones. Viewed i…
Regularization and confounding in linear regression for treatment effect estimation
P. Richard Hahn, Carlos M. Carvalho, Jingyu He +1
This paper investigates the use of regularization priors in the context of treatment effect estimation using observational data where the number of control variables is large relat…