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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/133658

    Title: Macroeconomic Forecasting Using Approximate Factor Models with Outliers
    Authors: 顏佑銘
    Yen, Yu-Min
    Chou, Ray Yeutien
    Yen, Tso-Jung
    Contributors: 國貿系
    Keywords: Approximate factor model;Macroeconomic forecast;Multivariate time series;Outlier;Principal component analysis
    Date: 2020-04
    Issue Date: 2021-01-21 09:51:20 (UTC+8)
    Abstract: 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.
    Relation: International Journal of Forecasting, Vol.36, No.2, pp.267-291
    Data Type: article
    DOI 連結: https://doi.org/10.1016/j.ijforecast.2019.04.020
    DOI: 10.1016/j.ijforecast.2019.04.020
    Appears in Collections:[國際經營與貿易學系 ] 期刊論文

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