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A robust algorithm for parameter estimation in smooth transition autoregressive models

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  • Bekiros, Stelios D.

Abstract

Finding a precise estimate for the smoothness parameter of LSTAR models is notoriously difficult. This paper introduces a robust estimation method for the transition and autoregressive parameters of STAR models, comprising gradient descent and singular value decomposition to account for heteroscedastic noise.

Suggested Citation

  • Bekiros, Stelios D., 2009. "A robust algorithm for parameter estimation in smooth transition autoregressive models," Economics Letters, Elsevier, vol. 103(1), pages 36-38, April.
  • Handle: RePEc:eee:ecolet:v:103:y:2009:i:1:p:36-38
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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. LeBaron, Blake, 1992. "Some Relations between Volatility and Serial Correlations in Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 65(2), pages 199-219, April.
    3. Hansen Bruce E., 1997. "Inference in TAR Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(1), pages 1-16, April.
    4. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-368, July.
    5. White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-161, January.
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    Cited by:

    1. Novella Maugeri, 2014. "Some Pitfalls in Smooth Transition Models Estimation: A Monte Carlo Study," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 339-378, October.
    2. repec:spr:annopr:v:262:y:2018:i:2:d:10.1007_s10479-015-2078-z is not listed on IDEAS

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