ASYMPTOTIC THEORY FOR MAXIMUM LIKELIHOOD ESTIMATION OF THE MEMORY PARAMETER IN STATIONARY GAUSSIAN PROCESSES
Consistency, asymptotic normality, and efficiency of the maximum likelihood estimator for stationary Gaussian time series were shown to hold in the short memory case by Hannan (1973, <italic>Journal of Applied Probability</italic> 10, 130–145) and in the long memory case by Dahlhaus (1989, <italic>Annals of Statistics</italic> 34, 1045–1047). In this paper we extend these results to the entire stationarity region, including the case of antipersistence and noninvertibility.
Year of publication: |
2012
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Authors: | Lieberman, Offer ; Rosemarin, Roy ; Rousseau, Judith |
Published in: |
Econometric Theory. - Cambridge University Press. - Vol. 28.2012, 02, p. 457-470
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Publisher: |
Cambridge University Press |
Description of contents: | Abstract [journals.cambridge.org] |
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