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Economic and Financial Data as Nonlinear Processes

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The Stock Market: Bubbles, Volatility, and Chaos

Abstract

The last decade has witnessed a resurgence of interest in nonlinearity as applied to a wide variety of disciplines including economics. This renewed interest has been occasioned by the development of new research tools and the growing disenchantment, in economics at least, with the forecasts that our linear models have produced. We have now come to the realization that models of money demand and supply of the stock market, of national aggregates, such as GNP, investment in plant and equipment, consumption, and savings, provide reasonable forecasts only in those cases where the models are not really needed, that is, where the next period is simply last period plus an approximately constant growth factor, or plus a random change with zero mean.

The support and technical services of the C. V. Starr Center for Applied Economics is gratefully acknowledged. I am pleased to thank Connie Dorfschmidt, Gerald Dwyer, and Charles de Bartolome for constructive comments.

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  1. James B. Ramsey
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Editors and Affiliations

  1. Clemson University and Federal Reserve Bank of St. Louis, USA

    Gerald P. Dwyer Jr.

  2. Southern Illinois University at Edwardsville and Federal Reserve Bank of St. Louis, USA

    R. W. Hafer

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Ramsey, J.B. (1990). Economic and Financial Data as Nonlinear Processes. In: Dwyer, G.P., Hafer, R.W. (eds) The Stock Market: Bubbles, Volatility, and Chaos. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-7881-3_4

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  • DOI: https://doi.org/10.1007/978-94-015-7881-3_4

  • Publisher Name: Springer, Dordrecht

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