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Are linear models really unuseful to describe business cycle data?

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  • Artur Silva Lopes
  • Gabriel Florin Zsurkis

Abstract

We use first differenced logged quarterly series for the GDP of 29 countries and the euro area to assess the need to use non-linear models to describe business cycle dynamic behaviour. Our approach is model (estimation)-free, based on testing only. We aim to maximize power to detect non-linearities while, simultaneously, avoiding the pitfalls of data mining. The evidence we find does not support some descriptions because the presence of significant non-linearities is observed for two-thirds of the countries only. Linear models cannot be simply dismissed as they are frequently useful. Contrarily to common knowledge, non-linear business cycle variation does not seem to be a universal, undisputable and clearly dominant stylized fact. This finding is particularly surprising for the U.S. case. Some support for non-linear dynamics for some further countries is obtained indirectly, through unit root tests, but this can hardly be invoked to support non-linearity in classical business cycles.

Suggested Citation

  • Artur Silva Lopes & Gabriel Florin Zsurkis, 2019. "Are linear models really unuseful to describe business cycle data?," Applied Economics, Taylor & Francis Journals, vol. 51(22), pages 2355-2376, May.
    • Handle: RePEc:taf:applec:v:51:y:2019:i:22:p:2355-2376
    DOI: 10.1080/00036846.2018.1495825
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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