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Forecasting with Non-spurious Factors in U.S. Macroeconomic Time Series

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  • Yohei Yamamoto

Abstract

Time instability in factor loadings can induce an overfitting problem in forecasting analyses since the structural change in factor loadings inflates the number of principal components and thus produces spurious factors. This paper proposes an algorithm to estimate non-spurious factors by identifying the set of observations with stable factor loadings based on the recursive procedure suggested by Inoue and Rossi (2011). I found that 51 out of 132 U.S. macroeconomic time series of Stock and Watson (2005) have stable factor loadings. Although crude principal components provide eight or more factors, there are only one or two non-spurious factors. The forecasts using non-spurious factors significantly improve out-of-sample performance.

Suggested Citation

  • Yohei Yamamoto, 2013. "Forecasting with Non-spurious Factors in U.S. Macroeconomic Time Series," Global COE Hi-Stat Discussion Paper Series gd12-280, Institute of Economic Research, Hitotsubashi University.
    • Handle: RePEc:hst:ghsdps:gd12-280
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    File URL: http://gcoe.ier.hit-u.ac.jp/research/discussion/2008/pdf/gd12-280.pdf
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    References listed on IDEAS

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    1. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    2. Xu Cheng & Zhipeng Liao & Frank Schorfheide, 2016. "Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1511-1543.
    3. Marc P. Giannoni & Jean Boivin, 2005. "DSGE Models in a Data-Rich Environment," Computing in Economics and Finance 2005 431, Society for Computational Economics.
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    Cited by:

    1. Aslanidis, Nektarios & Hartigan, Luke, 2016. "Is the Assumption of Linearity in Factor Models too Strong in Practice?," Working Papers 2072/261531, Universitat Rovira i Virgili, Department of Economics.
    2. Luke Hartigan, 2015. "Changes in the Factor Structure of the U.S. Economy: Permanent Breaks or Business Cycle Regimes?," Discussion Papers 2015-17, School of Economics, The University of New South Wales.
    3. Bai, Jushan & Han, Xu & Shi, Yutang, 2020. "Estimation and inference of change points in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 219(1), pages 66-100.
    4. Yohei Yamamoto & Naoko Hara, 2022. "Identifying factor‐augmented vector autoregression models via changes in shock variances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 722-745, June.
    5. Esther Ruiz & Pilar Poncela, 2022. "Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components," Foundations and Trends(R) in Econometrics, now publishers, vol. 12(2), pages 121-231, November.

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    More about this item

    Keywords

    dynamic factor model; principal components; structural change; spurious factors; out-of-sample forecasts; overfitting;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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