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Estimation in nonlinear time series models

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  • Tjøstheim, Dag
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    Abstract

    A general framework for analyzing estimates in nonlinear time series is developed. General conditions for strong consistency and asymptotic normality are derived both for conditional least squares and maximum likelihood types estimates. Ergodie strictly stationary processes are studied in the first part and certain nonstationary processes in the last part of the paper. Examples are taken from most of the usual classes of nonlinear time series models.

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    Bibliographic Info

    Article provided by Elsevier in its journal Stochastic Processes and their Applications.

    Volume (Year): 21 (1986)
    Issue (Month): 2 (February)
    Pages: 251-273

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    Handle: RePEc:eee:spapps:v:21:y:1986:i:2:p:251-273

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    Related research

    Keywords: asymptotic normality conditional least square consistency maximum likelihood nonlinear time series;

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