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Noninvertibility and Pseudo-MaximumLikelihood Estimation of Misspecified ARMAModels

Published online by Cambridge University Press:  11 February 2009

Abstract

Recently Tanaka and Satchell [11] investigated thelimiting properties of local maximizers of theGaussian pseudo-likelihood function of amisspecified moving average model of order one incase the spectral density of the data process has azero at frequency zero. We show that pseudo-maximumlikelihood estimators in the narrower sense, thatis, global maximizers of the Gaussianpseudo-likelihood function, may exhibit behaviordrastically different from that of the localmaximizers. Some general results on the limitingbehavior of pseudo-maximum likelihood estimators inpotentially misspecified ARMA models are alsopresented.

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Type
Research Article
Copyright
Copyright © Cambridge University Press 1991

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References

REFERENCEs

1.Dahlhaus, R. & Pötscher, B.M. Convergence results for maximum likelihood type estimators in multivariable ARMA models II. Journal of Multivariate Analysis 30 (1989): 241244.10.1016/0047-259X(89)90037-7Google Scholar
2. Deistler, M. The properties of the parameterization of ARMAX systems and their relevance for structural estimation. Econometrica 51 (1983): 11871207.10.2307/1912058Google Scholar
3. Deistler, M., Dunsmuir, W. & Hannan, E.J. Vector linear time series models: corrections and extensions. Advances in Applied Probability 10 (1978): 360372.10.2307/1426940Google Scholar
4. Deistler, M. & Pötscher, B.M.. The behaviour of the likelihood function for ARMA models. Advances in Applied Probability 16 (1984): 843865.10.1017/S0001867800022965Google Scholar
5. Dunsmuir, W. & Hannan, E.J.. Vector linear time series models. Advances in Applied Probability 8 (1976): 339364.10.2307/1425908Google Scholar
6. Hannan, E.J. The asymptotic theory of linear time series models. Journal of Applied Probability 10 (1973): 130145.10.2307/3212501Google Scholar
7. Hannan, E.J. &Deistler, M.. TheStatistical Theory of Linear Systems. New York: Wiley, 1988.Google Scholar
8. Kabaila, P. Parameter values of ARMA models minimising the one-step-ahead prediction error when the true system is not in the model set. Journal of Applied Probability 20 (1983): 405408.10.2307/3213814Google Scholar
9. Pötscher, B.M. Convergence results for maximum likelihood type estimators in multivariable ARMA models. Journal of Multivariate Analysis 21 (1987): 2952.10.1016/0047-259X(87)90097-2Google Scholar
10.Pötscher, B.M. Estimation of autoregressive moving average order given an infinite number of models and approximation of spectral densities. Journal of Time Series Analysis 11 (1990): 165179.10.1111/j.1467-9892.1990.tb00049.xGoogle Scholar
11. Tanaka, K. & Satchell, S.E.. Asymptotic properties of the maximum-likelihood and nonlinear least-squares estimator for noninvertible moving average models. Econometric 5 (1989): 333353.Google Scholar