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Partially adaptive estimation of nonlinear models via a normal mixture

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  • R. F. Phillips

Abstract

This paper extends the partially adaptive method Phillips (1994) provided for linear models to nonlinear models. Asymptotic results are established under conditions general enough they cover both cross-sectional and time series applications. The sampling efficiency of the new estimator is illustrated in a small Monte Carlo study in which the parameters of an autoregressive moving average are estimated. The study indicates that, for non-normal distributions, the new estimator improves on the nonlinear least squares estimator in terms of efficiency.

Suggested Citation

  • R. F. Phillips, 1999. "Partially adaptive estimation of nonlinear models via a normal mixture," Econometric Reviews, Taylor & Francis Journals, vol. 18(2), pages 141-167.
    • Handle: RePEc:taf:emetrv:v:18:y:1999:i:2:p:141-167
    DOI: 10.1080/07474939908800437
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    References listed on IDEAS

    as
    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    2. Phillips, Robert F., 1994. "Partially adaptive estimation via a normal mixture," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 123-144.
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    More about this item

    Keywords

    ARMA process; nonlinear regression model; quasi maximum likelihood; JEL Classifications:C13; C20;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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