Modeling Long Cycles

Natasha Kang and Vadim Marmer (vadim.marmer@ubc.ca)

Papers from arXiv.org

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.

Date: 2020-10, Revised 2023-09
New Economics Papers: this item is included in nep-fdg and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Journal of Econometrics, 242(1), May 2024, 105751

Downloads: (external link)
http://arxiv.org/pdf/2010.13877 Latest version (application/pdf)

Related works:
Journal Article: Modeling long cycles (2024)
Working Paper: Modeling Long Cycles (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2010.13877

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators (help@arxiv.org).