Are linear models really unuseful to describe business cycle data?

C-Tier
Journal: Applied Economics
Year: 2019
Volume: 51
Issue: 22
Pages: 2355-2376

Authors (2)

Artur Silva Lopes Gabriel Florin Zsurkis (not in RePEc)

Score contribution per author:

0.503 = (α=2.01 / 2 authors) × 0.5x C-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:taf:applec:v:51:y:2019:i:22:p:2355-2376
Journal Field
General
Author Count
2
Added to Database
2026-01-29