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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;Macroeconomic forecast;Multivariate time series;Outlier;Principal component analysis
日期 2020-04
上傳時間 21-Jan-2021 09:51:20 (UTC+8)
摘要 In this paper we consider estimating an approximate factor model in which candidate predictors are subject to sharp spikes such as outliers or jumps. Given that these sharp spikes are assumed to be rare, we formulate the estimation problem as a penalized least squares problem by imposing a norm penalty function on those sharp spikes. Such a formulation allows us to disentangle the sharp spikes from the common factors and estimate them simultaneously. Numerical values of the estimates can be obtained by solving a principal component analysis (PCA) problem and a one-dimensional shrinkage estimation problem iteratively. In addition, it is easy to incorporate methods for selecting the number of common factors in the iterations. We compare our method with PCA by conducting simulation experiments in order to examine their finite-sample performances. We also apply our method to the prediction of important macroeconomic indicators in the U.S., and find that it can deliver performances that are comparable to those of the PCA method.
關聯 International Journal of Forecasting, Vol.36, No.2, pp.267-291
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) 2020-04
dc.date.accessioned 21-Jan-2021 09:51:20 (UTC+8)-
dc.date.available 21-Jan-2021 09:51:20 (UTC+8)-
dc.date.issued (上傳時間) 21-Jan-2021 09:51:20 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/133658-
dc.description.abstract (摘要) In this paper we consider estimating an approximate factor model in which candidate predictors are subject to sharp spikes such as outliers or jumps. Given that these sharp spikes are assumed to be rare, we formulate the estimation problem as a penalized least squares problem by imposing a norm penalty function on those sharp spikes. Such a formulation allows us to disentangle the sharp spikes from the common factors and estimate them simultaneously. Numerical values of the estimates can be obtained by solving a principal component analysis (PCA) problem and a one-dimensional shrinkage estimation problem iteratively. In addition, it is easy to incorporate methods for selecting the number of common factors in the iterations. We compare our method with PCA by conducting simulation experiments in order to examine their finite-sample performances. We also apply our method to the prediction of important macroeconomic indicators in the U.S., and find that it can deliver performances that are comparable to those of the PCA method.
dc.format.extent 613019 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) International Journal of Forecasting, Vol.36, No.2, pp.267-291
dc.subject (關鍵詞) Approximate factor model;Macroeconomic forecast;Multivariate time series;Outlier;Principal component analysis
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