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Is the Assumption of Linearity in Factor Models too Strong in Practice?

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  • Nektarios Aslanidis

    (Universitat Rovira i Virgili, CREIP)

  • Luke Hartigan

    (School of Economics, UNSW Business School, UNSW)

Abstract

The assumption of linearity of factor models is implicit in all empirical applications used in macroeconomic analysis. We test this assumption in a more general setting than previously considered using a well-studied macroeconomic dataset on the U.S. economy, and find strong evidence in support for regime-switching type non-linearity. Furthermore, we show non-linearity is strongly concentrated in certain groups (such as financial variables). Our results, which are robust to serial dependence, suggest the assumption of linearity underpinning factor models might be too strong and gives further support towards developing models which explicitly account for non-linearity.

Suggested Citation

  • Nektarios Aslanidis & Luke Hartigan, 2016. "Is the Assumption of Linearity in Factor Models too Strong in Practice?," Discussion Papers 2016-03, School of Economics, The University of New South Wales.
  • Handle: RePEc:swe:wpaper:2016-03
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    File URL: http://research.economics.unsw.edu.au/RePEc/papers/2016-03.pdf
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    More about this item

    Keywords

    Factor Model Non-linearity; Regime Change; Transition Variables; LM test;
    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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