Modeling long cycles

Da Natasha Kang and Vadim Marmer ()

Journal of Econometrics, 2024, vol. 242, issue 1

Abstract: Recurrent boom-and-bust cycles are a salient feature of economic and financial history. Cycles found in the data are stochastic, often highly persistent, and span substantial fractions of the sample size. We refer to such cycles as “long”. In this paper, we develop a novel approach to modeling cyclical behavior specifically designed to capture long cycles. We show that existing inferential procedures may produce misleading results in the presence of long cycles and propose a new econometric procedure for the inference on the cycle length. Our procedure is asymptotically valid regardless of the cycle length. We apply our methodology to a set of macroeconomic and financial variables for the U.S. We find evidence of long stochastic cycles in the standard business cycle variables, as well as in credit and house prices. However, we rule out the presence of stochastic cycles in asset market data. Moreover, according to our result, financial cycles, as characterized by credit and house prices, tend to be twice as long as business cycles.

Keywords: Stochastic cycles; Autoregressive processes; Local-to-unity asymptotics; Confidence sets; Business cycle; Financial cycle (search for similar items in EconPapers)
JEL-codes: C12 C22 C5 E32 E44 (search for similar items in EconPapers)
Date: 2024
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Related works:
Working Paper: Modeling Long Cycles (2023)
Working Paper: Modeling Long Cycles (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:242:y:2024:i:1:s0304407624000976

DOI: 10.1016/j.jeconom.2024.105751

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