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TAC Method for Fitting Exponential Autoregressive Models and Others: Applications in Economy and Finance

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  • Javier Cabello S'anchez
  • Juan Antonio Fern'andez Torvisco
  • Mariano R. Arias

Abstract

There are a couple of purposes in this paper: to study a problem of approximation with exponential functions and to show its relevance for the economic science. We present results that completely solve the problem of the best approximation by means of exponential functions and we will be able to determine what kind of data is suitable to be fitted. Data will be approximated using TAC (implemented in the R-package nlstac), a numerical algorithm for fitting data by exponential patterns without initial guess designed by the authors. We check one more time the robustness of this algorithm by successfully applying it to two very distant areas of economy: demand curves and nonlinear time series. This shows TAC's utility and highlights how far this algorithm could be used.

Suggested Citation

  • Javier Cabello S'anchez & Juan Antonio Fern'andez Torvisco & Mariano R. Arias, 2024. "TAC Method for Fitting Exponential Autoregressive Models and Others: Applications in Economy and Finance," Papers 2402.04138, arXiv.org.
    • Handle: RePEc:arx:papers:2402.04138
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    References listed on IDEAS

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    1. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    2. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    3. Harvey J. Greenberg & William P. Pierskalla, 1971. "A Review of Quasi-Convex Functions," Operations Research, INFORMS, vol. 19(7), pages 1553-1570, December.
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