2 citations · 7 across the 8 of their papers we have counts for
8 papers
Bias Correction in Factor-Augmented Regression Models with Weak Factors
Peiyun Jiang, Yoshimasa Uematsu, Takashi Yamagata
In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the factors extracted from a large number of $…
Revisiting Asymptotic Theory for Principal Component Estimators of Approximate Factor Models
Peiyun Jiang, Yoshimasa Uematsu, Takashi Yamagata
It is well known that approximate factor models exhibit rotation indeterminacy. Principal component (PC) estimators are typically analyzed relative to a rotated factor-loading repr…
Moment-Based Adjustments of Statistical Inference in High-Dimensional Generalized Linear Models
Kazuma Sawaya, Yoshimasa Uematsu, Masaaki Imaizumi
We develop a statistical inference method for generalized linear models (GLMs) in high-dimensional settings, where the number of unknown coefficients is of the same order as th…
Discovering the Network Granger Causality in Large Vector Autoregressive Models
Yoshimasa Uematsu, Takashi Yamagata
This paper proposes novel inferential procedures for discovering the network Granger causality in high-dimensional vector autoregressive models. In particular, we mainly offer two…
IPAD: Stable Interpretable Forecasting with Knockoffs Inference
Yingying Fan, Jinchi Lv, Mahrad Sharifvaghefi +1
Interpretability and stability are two important features that are desired in many contemporary big data applications arising in economics and finance. While the former is enjoyed…
SOFAR: large-scale association network learning
Yoshimasa Uematsu, Yingying Fan, Kun Chen +2
Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insights can be enabled by understan…
Macroeconomic Forecasting and Variable Selection with a Very Large Number of Predictors: A Penalized Regression Approach
Yoshimasa Uematsu, Shinya Tanaka
This paper studies macroeconomic forecasting and variable selection using a folded-concave penalized regression with a very large number of predictors. The penalized regression app…
Penalized Likelihood Estimation in High-Dimensional Time Series Models and its Application
Yoshimasa Uematsu
This paper presents a general theoretical framework of penalized quasi-maximum likelihood (PQML) estimation in stationary multiple time series models when the number of parameters…