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題名 Macroeconomic Forecasting Using Approximate Factor Models with Outliers
作者 顏佑銘*
Yen, Yu-Min
Chou, Ray Yeutien
Yen, Tso-Jung
貢獻者 國貿系
關鍵詞 Approximate Factor Model ; PCA ; Norm Penalty
日期 2019-04
上傳時間 26-Feb-2020 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
資料類型 article
DOI https://doi.org/10.1016/j.ijforecast.2019.04.020
dc.contributor 國貿系
dc.creator (作者) 顏佑銘*
dc.creator (作者) Yen, Yu-Min
dc.creator (作者) Chou, Ray Yeutien
dc.creator (作者) Yen, Tso-Jung
dc.date (日期) 2019-04
dc.date.accessioned 26-Feb-2020 15:24:50 (UTC+8)-
dc.date.available 26-Feb-2020 15:24:50 (UTC+8)-
dc.date.issued (上傳時間) 26-Feb-2020 15:24:50 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/128756-
dc.description.abstract (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.
dc.format.extent 613019 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) International Journal of Forecasting
dc.subject (關鍵詞) Approximate Factor Model ; PCA ; Norm Penalty
dc.title (題名) Macroeconomic Forecasting Using Approximate Factor Models with Outliers
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1016/j.ijforecast.2019.04.020
dc.doi.uri (DOI) https://doi.org/10.1016/j.ijforecast.2019.04.020