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An elementary business cycle mechanism: learning from Harrod and Kaldor

Author

Listed:
  • Franke Reiner

    (Department of Economics, University of Kiel, Wilhelm-Seelig-Platz 1,Kiel 24118, Germany)

Abstract

Drawing on Harrod, Kalecki and Kaldor, this paper seeks to revive the view that ceteris paribus firms reduce investment if they have already built up high capacities relative to their assessment of the normal market potential. This reaction establishes a fundamental stabilizing mechanism for the economy. The paper adapts the idea to the growth context of a neo-Kaleckian baseline model, where its destabilizing Harrodian sentiment adjustments are specified in a canonical and microfounded way that introduces a natural nonlinearity into the global dynamics. Supposing local instability, the resulting two-dimensional system is shown to generate persistent self-sustaining cyclical behaviour. In addition, the model is numerically calibrated to period and amplitude of the US business cycle. On the whole, based on an old and almost forgotten conception, the paper advances a most elementary approach to business cycle modelling.

Suggested Citation

  • Franke Reiner, 2019. "An elementary business cycle mechanism: learning from Harrod and Kaldor," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(5), pages 1-18, December.
  • Handle: RePEc:bpj:sndecm:v:23:y:2019:i:5:p:18:n:3
    DOI: 10.1515/snde-2017-0106
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    More about this item

    Keywords

    Harrodian instability; capacity effects; nonlinear sentiment adjustments; phase diagrams; numerical calibration;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • E12 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Keynes; Keynesian; Post-Keynesian; Modern Monetary Theory
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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