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PARAMETER ESTIMATION WITH AUTOCORRELATED DISTURBANCES
Author(s) -
Bennett James T.
Publication year - 1972
Publication title -
metroeconomica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.256
H-Index - 29
eISSN - 1467-999X
pISSN - 0026-1386
DOI - 10.1111/j.1467-999x.1972.tb00202.x
Subject(s) - estimator , autoregressive model , autocorrelation , ordinary least squares , mathematics , maximum likelihood , least squares function approximation , estimation theory , statistics , mathematical optimization , maximum likelihood sequence estimation , sampling interval
A bstract Available methods for dealing with autocorrelated disturbanecs are unsatisfactory. They are computationally cumbersome and lead to estimators that have only weak asymptotic properties. Maximum likelihood estimation of all parameters is theoretically preferable, but has been unpopular due to the practical difficulties of numerical optimization in a multidimensional space. In this paper it is shown that the computational aspect of maximum likelihood estimation can be greatly simplified. The numerical search is confined to the interval (— 1, 1) of the autocorrelation parameter. The method is applicable to a single static equation as well as to autoregressive models with one or more equations. Some sampling experiments demonstrate the superiority of the maximum likelihood estimator over ordinary least squares, generalized least squares, and one other estimator in small samples.