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Disentangling Structural Breaks in Factor Models for Macroeconomic Data

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Listed:
  • Bonsoo Koo
  • Benjamin Wong
  • Ze-Yu Zhong

Abstract

Through a routine normalization of the factor variance, standard methods for estimating factor models in macroeconomics do not distinguish between breaks of the factor variance and factor loadings. We argue that it is important to distinguish between structural breaks in the factor variance and loadings within factor models commonly employed in macroeconomics as both can lead to markedly different interpretations when viewed via the lens of the underlying dynamic factor model. We then develop a projection-based decomposition that leads to two standard and easy-to-implement Wald tests to disentangle structural breaks in the factor variance and factor loadings. Applying our procedure to U.S. macroeconomic data, we find evidence of both types of breaks associated with the Great Moderation and the Great Recession. Through our projection-based decomposition, we estimate that the Great Moderation is associated with an over 60% reduction in the total factor variance, highlighting the relevance of disentangling breaks in the factor structure.

Suggested Citation

  • Bonsoo Koo & Benjamin Wong & Ze-Yu Zhong, 2023. "Disentangling Structural Breaks in Factor Models for Macroeconomic Data," Papers 2303.00178, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2303.00178
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    References listed on IDEAS

    as
    1. Han, Xu & Inoue, Atsushi, 2015. "Tests For Parameter Instability In Dynamic Factor Models," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1117-1152, October.
    2. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    3. Chen, Liang, 2015. "Estimating the common break date in large factor models," Economics Letters, Elsevier, vol. 131(C), pages 70-74.
    4. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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    More about this item

    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
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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