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政大機構典藏 > 商學院 > 國際經營與貿易學系 > 期刊論文 > Item 140.119/128756

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题名:	Macroeconomic Forecasting Using Approximate Factor Models with Outliers
作者:	顏佑銘* Yen, Yu-Min Chou, Ray Yeutien Yen, Tso-Jung
贡献者:	國貿系
关键词:	Approximate Factor Model;PCA;Norm Penalty
日期:	2019-04
上传时间:	2020-02-26 15:24:50 (UTC+8)
摘要:	Approximate factor models and their extensions are widely used in forecasting and economic analysis due to their ability to extracting useful information from a large number of relevant variables. In these models, candidate predictors are typically subject to some common components. In this paper, we consider to efficiently estimate an approximate factor model in which the candidate predictors are additionally subject to idiosyncratic large uncommon components such as jumps or outliers. By assuming that occurrences of the uncommon components are rare, we propose an estimation procedure to simultaneously disentangle and estimate the common and uncommon components. We formulate the estimation problem as a penalized least squares problem in which a norm penalty function is imposed on the uncommon components. To solve the estimation problem, we propose an algorithm, which iteratively solves a principal component analysis (PCA) problem and a one dimensional shrinkage estimation problem. The algorithm is flexible in incorporating methods for selecting the number of common components. We then compare finite-sample efficiency of the proposed method and traditional PCA method with simulations. We also demonstrate performances of the proposed method with empirical applications on predicting yearly growths of important macroeconomic indicators.
關聯:	International Journal of Forecasting
数据类型:	article
DOI 連結:	https://doi.org/10.1016/j.ijforecast.2019.04.020
DOI:	10.1016/j.ijforecast.2019.04.020
显示于类别:	[國際經營與貿易學系 ] 期刊論文

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