Penalized Likelihood Estimation in High-Dimensional Time Series Models and its Application
Statistics Theory
2017-04-28 v3 Applications
Statistics Theory
Abstract
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 possibly diverges. We show the oracle property of the PQML estimator under high-level, but tractable, assumptions, comprising the first half of the paper. Utilizing these results, we propose in the latter half of the paper a method of sparse estimation in high-dimensional vector autoregressive (VAR) models. Finally, the usability of the sparse high-dimensional VAR model is confirmed with a simulation study and an empirical analysis on a yield curve forecast.
Cite
@article{arxiv.1504.06706,
title = {Penalized Likelihood Estimation in High-Dimensional Time Series Models and its Application},
author = {Yoshimasa Uematsu},
journal= {arXiv preprint arXiv:1504.06706},
year = {2017}
}
Comments
This manuscript includes some theoretically insufficient points that will be fixed