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Making dynamic modelling effective in economics

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  • McCauley, Joseph L.

Abstract

Mathematics has been extremely effective in physics, but not in economics beyond finance. To establish economics as science we should follow the Galilean method and try to deduce mathematical models of markets from empirical data, as has been done for financial markets. Financial markets are nonstationary. This means that 'value' is subjective. Nonstationarity also means that the form of the noise in a market cannot be postulated a priroi, but must be deduced from the empirical data. I discuss the essence of complexity in a market as unexpected events, and end with a biological speculation about market growth.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 2130.

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Date of creation: Mar 2004
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Handle: RePEc:pra:mprapa:2130

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Related research

Keywords: Economics; fniancial markets; stochastic process; Markov process; complex systems;

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  1. A. P. Thirlwall, 1983. "Introduction," Journal of Post Keynesian Economics, M.E. Sharpe, Inc., vol. 5(3), pages 341-344, April.
  2. Per Bak & Simon F. Norrelykke & Martin Shubik, 1998. "The Dynamics of Money," Papers cond-mat/9811094, arXiv.org, revised May 1999.
  3. A. Meltzer & Peter Ordeshook & Thomas Romer, 1983. "Introduction," Public Choice, Springer, vol. 41(1), pages 1-5, January.
  4. Gemunu H. Gunaratne & Joseph L. McCauley, 2002. "A theory for Fluctuations in Stock Prices and Valuation of their Options," Papers cond-mat/0209475, arXiv.org.
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