Model Selection for Unit-root Time Series with Many Predictors

• Main Authors: Shuo-Chieh Huang, 黃碩傑
• Other Authors: 銀慶剛
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/472y9a

碩士 === 國立臺灣大學 === 經濟學研究所 === 105 === Model selection for the autoregressive models with exogenous inputs (ARX models) is studied in this paper. In particular, we consider the situation where the series is possibly non-stationary and a large number of predictors (even larger than the sample size) is available. Inspired by Ing and Lai (2011)’s OGA+HDIC+Trim, we propose to replace the orthogonal greedy algorithm (OGA) by the partial least squares (PLS) as forward inclusion algorithm, which we call the PLS+HDIC+Trim. The PLS+HDIC+Trim has a strong model selection ability even when the regressors are non-stationary. Therefore, this new method is still valid without any prior knowledge of the integration order or under models that are not difference-stationary. Also, we propose an order selection scheme that can select the integration order for difference- stationary models. Simulation studies also showed that the PLS+HDIC+Trim outperformed other high-dimensional methods. We apply this new method to U.S. macroeconomic data.