Modeling long cycles

doi:10.1016/j.jeconom.2024.105751

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Modeling long cycles

2024-05-01

PRE

Da Natasha Kang *

Vadim Marmer

DOI：10.1016/j.jeconom.2024.105751

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Abstract

En 中文

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