Are linear models really unuseful to describe business cycle data?

Artur Silva Lopes () and Gabriel Florin Zsurkis

Applied Economics, 2019, vol. 51, issue 22, 2355-2376

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.

Date: 2019
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Working Paper: Are linear models really unuseful to describe business cycle data? (2017)
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DOI: 10.1080/00036846.2018.1495825

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