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Bayesian estimation and forecasting in nonlinear models : application to an LSTAR model

Author

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  • Anne Peguin-Feissolle

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper considers the Bayesian estimation and prediction in a non-linear model by means of Monte Carlo integration with importance sampling. The importance function is derived from a first-order Taylor series expansion of the non-linear conditional expectation of the endogenous variable. The method is applied to an LSTAR model with an artificial sample.

Suggested Citation

  • Anne Peguin-Feissolle, 1994. "Bayesian estimation and forecasting in nonlinear models : application to an LSTAR model," Post-Print hal-00390208, HAL.
  • Handle: RePEc:hal:journl:hal-00390208
    DOI: 10.1016/0165-1765(94)00478-1
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    Cited by:

    1. Anoop Chaturvedi & Shivam Jaiswal, 2020. "Bayesian Estimation and Unit Root Test for Logistic Smooth Transition Autoregressive Process," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(4), pages 733-745, December.

    More about this item

    Keywords

    Bayesian estimation; LSTAR;

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