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Estimators for alternating nonlinear autoregression

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  • Müller, Ursula U.
  • Schick, Anton
  • Wefelmeyer, Wolfgang

Abstract

Suppose we observe a time series that alternates between different nonlinear autoregressive processes. We give conditions under which the model is locally asymptotically normal, derive a characterization of efficient estimators for differentiable functionals of the model, and use it to construct efficient estimators for the autoregression parameters and the innovation distributions. Surprisingly, the estimators for the autoregression parameters can be improved if we know that the innovation densities are equal.

Suggested Citation

  • Müller, Ursula U. & Schick, Anton & Wefelmeyer, Wolfgang, 2009. "Estimators for alternating nonlinear autoregression," Journal of Multivariate Analysis, Elsevier, vol. 100(2), pages 266-277, February.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:2:p:266-277
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    References listed on IDEAS

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    1. Anton Schick & Wolfgang Wefelmeyer, 2002. "Estimating the Innovation Distribution in Nonlinear Autoregressive Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 245-260, June.
    2. Bhattacharya, Rabi & Lee, Chanho, 1995. "On geometric ergodicity of nonlinear autoregressive models," Statistics & Probability Letters, Elsevier, vol. 22(4), pages 311-315, March.
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