Modeling Long Cycles

TL;DR

This paper introduces a new econometric method to accurately model and infer the length of long economic and financial cycles, revealing that financial cycles are typically twice as long as standard business cycles.

Contribution

It develops a novel inference procedure specifically designed for long cycles, addressing limitations of existing methods and applying it to macroeconomic and financial data.

Findings

Long stochastic cycles are present in business, credit, and house prices.

Financial cycles are approximately twice as long as business cycles.

No stochastic cycles are found in asset market data.

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.