5 papers
High-Dimensional Spatial Arbitrage Pricing Theory with Heterogeneous Interactions
Zhaoxing Gao, Sihan Tu, Ruey S. Tsay
This paper investigates estimation and inference of a Spatial Arbitrage Pricing Theory (SAPT) model that integrates spatial interactions with multi-factor analysis, accommodating b…
High-Dimensional Matrix-Variate Diffusion Index Models for Time Series Forecasting
Zhiren Ma, Qian Zhao, Riquan Zhang +1
This paper proposes a novel diffusion-index model for forecasting when predictors are high-dimensional matrix-valued time series. We apply an -PCA method to extract low-dimensio…
A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting
Sihan Tu, Zhaoxing Gao
Factor-based forecasting using Principal Component Analysis (PCA) is an effective machine learning tool for dimension reduction with many applications in statistics, economics, and…
Sparse Asymptotic PCA: Identifying Sparse Latent Factors Across Time Horizon in High-Dimensional Time Series
Zhaoxing Gao
This paper introduces a novel sparse latent factor modeling framework using sparse asymptotic Principal Component Analysis (APCA) to analyze the co-movements of high-dimensional pa…
Banded Spatio-Temporal Autoregressions
Zhaoxing Gao, Yingying Ma, Hansheng Wang +1
We propose a new class of spatio-temporal models with unknown and banded autoregressive coefficient matrices. The setting represents a sparse structure for high-dimensional spatial…