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Is the assumption of constant factor loadings too strong in practice?

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  • Aslanidis, Nektarios
  • Hartigan, Luke

Abstract

The assumption of constant loadings when estimating factor models is implicit in all empirical applications used in macroeconomics. We test this assumption explicitly in a general smooth transition loadings setting using a well-studied macroeconomic dataset on the U.S. economy. Our proposed testing approach has reasonable finite sample properties relative to alternatives that allow for an abrupt change in the factor loadings. In an empirical application, we find evidence in support of non-constancy in factor loadings and show that it is mainly concentrated in certain sections of the economy (such as financial variables). Overall, our findings provide additional support toward developing factor models, which explicitly account for non-constant factor loadings.

Suggested Citation

  • Aslanidis, Nektarios & Hartigan, Luke, 2021. "Is the assumption of constant factor loadings too strong in practice?," Economic Modelling, Elsevier, vol. 98(C), pages 100-108.
    • Handle: RePEc:eee:ecmode:v:98:y:2021:i:c:p:100-108
    DOI: 10.1016/j.econmod.2021.02.015
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    Cited by:

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    2. Mehmet Balcilar & Riza Demirer & Festus V. Bekun, 2021. "Flexible Time-Varying Betas in a Novel Mixture Innovation Factor Model with Latent Threshold," Mathematics, MDPI, vol. 9(8), pages 1-20, April.

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    More about this item

    Keywords

    Constancy in factor loadings; LM test; Transition variables; Smooth transition autoregression models;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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