Growth Trends and Systematic Patterns of Booms and Busts‐Testing 200 Years of Business Cycle Dynamics

Marlon Fritz, Thomas Gries and Yuanhua Feng

Oxford Bulletin of Economics and Statistics, 2019, vol. 81, issue 1, 62-78

Abstract: We study the dynamic pattern of business cycles using US GDP data between 1790 and 2015. To address difficulties in trend and cycle decomposition, we introduce a semiparametric estimation approach with an iterative plug‐in (IPI) algorithm for endogenous bandwidth selection. This algorithm identifies continuously moving growth trends with trend‐supporting growth periods. A simulation study demonstrates the value‐added of our trend identification. Afterwards, nonlinear SETAR models are fitted parametrically. Further, we test the trend using a recently developed test and the estimated SETAR models against their linear alternatives. The results indicate asymmetric characteristics during booms and busts.

Date: 2019
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https://doi.org/10.1111/obes.12267

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Working Paper: Growth Trends and Systematic Patterns of Booms and Busts - Testing 200 Years of Business Cycle Dynamics - (2016)
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