Stochastic perturbations of limit cycles in nonlinear business cycle models: Kaldor's theory revisited

This paper introduces and validates a new approximation method for stochastic perturbations of limit cycles, taking up Kaldor's nonlinear theory of business cycles. This method provides explicit formulas for approximating solutions and probability distributions in stochastic business cycle models. The performance of these formulas is verified through numerical analysis, and time series analysis is used to fit the model to the data. This study attempts to pave the way for synthesizing the “exogenous” and “endogenous” approaches to business cycles.

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Publication Details

Journal
Institutional Repositories DataBase (IRDB)
Published
2026-04-17
DOI
https://doi.org/10.24789/0002003630
Primary Topic
Economic Theory and Policy
Type
article
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article

Stochastic perturbations of limit cycles in nonlinear business cycle models: Kaldor's theory revisited

Hiroki Murakami
Institutional Repositories DataBase (IRDB)
Economic Theory and Policy
article

Stochastic perturbations of limit cycles in nonlinear business cycle models: Kaldor's theory revisited

Hiroki Murakami
article en

Abstract

This paper introduces and validates a new approximation method for stochastic perturbations of limit cycles, taking up Kaldor's nonlinear theory of business cycles. This method provides explicit formulas for approximating solutions and probability distributions in stochastic business cycle models. The performance of these formulas is verified through numerical analysis, and time series analysis is used to fit the model to the data. This study attempts to pave the way for synthesizing the “exogenous” and “endogenous” approaches to business cycles.

Institutional Repositories DataBase (IRDB)Vol. 424
Openalex Percentile: Top 3%
Economic Theory and Policy
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Stochastic perturbations of limit cycles in nonlinear business cycle models: Kaldor's theory revisited — Hiroki Murakami · Institutional Repositories DataBase (IRDB) (2026) | TGRS Research Map | TGRS