Exact maximum likelihood estimation of regression models with finite order moving average errors

Pagan, AR; Nicholls, D

Exact maximum likelihood estimation of regression models with finite order moving average errors

Pagan, AR Nicholls, D

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Publisher:: Wiley-Blackwell
Publication Type:: Journal Article
Citation:: Review of Economic Studies, 1976, 43 (135), pp. 383 - 387
Issue Date:: 1976-01

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Field	Value	Language
dc.contributor.author	Pagan, AR	en_US
dc.contributor.author	Nicholls, D	en_US
dc.date.issued	1976-01	en_US
dc.identifier.citation	Review of Economic Studies, 1976, 43 (135), pp. 383 - 387	en_US
dc.identifier.issn	0034-6527	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13903
dc.description.abstract	The article presents information on exact maximum likelihood estimation of regression models with finite order moving average errors. A number of procedures for the estimation of models with moving average error specifications already appear in the literature. All these methods are, basically, derived from a consideration of a function which dominates the likelihood function and which is, asymptotically, equivalent to it. Consequently these procedures are applicable to large sample situations. economist M.H. Pesaran, however, has considered the exact likelihood function and his procedure is applicable to small samples. Unfortunately his method is not one that can be easily extended to the case of moving averages of order higher than the first since, for these models, it is not a straightforward matter to set up the orthogonal transformation required to diagonalize the covariance matrix of the disturbance term. Yet, as economist K. Kang has emphasized, it is of some importance to be able to compute the exact maximum likelihood (ML) estimates, since the generalized least squares (GLS) estimates frequently imply a non-invertible process for the disturbance term, and, for identification purposes, it is necessary that the process be invertible. The article is directed at the situation arising when the GLS estimates do not satisfy invertibility whereas the ML estimates do, this lack of invertibility of the OLS estimates being brought about by the omission of the extra terms in the likelihood function	en_US
dc.publisher	Wiley-Blackwell	en_US
dc.relation.ispartof	Review of Economic Studies	en_US
dc.subject.classification	Economics	en_US
dc.title	Exact maximum likelihood estimation of regression models with finite order moving average errors	en_US
dc.type	Journal Article
utslib.citation.volume	135	en_US
utslib.citation.volume	43	en_US
utslib.for	1401 Economic Theory	en_US
utslib.for	14 Economics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	135	en_US
pubs.volume	43	en_US

Abstract:

The article presents information on exact maximum likelihood estimation of regression models with finite order moving average errors. A number of procedures for the estimation of models with moving average error specifications already appear in the literature. All these methods are, basically, derived from a consideration of a function which dominates the likelihood function and which is, asymptotically, equivalent to it. Consequently these procedures are applicable to large sample situations. economist M.H. Pesaran, however, has considered the exact likelihood function and his procedure is applicable to small samples. Unfortunately his method is not one that can be easily extended to the case of moving averages of order higher than the first since, for these models, it is not a straightforward matter to set up the orthogonal transformation required to diagonalize the covariance matrix of the disturbance term. Yet, as economist K. Kang has emphasized, it is of some importance to be able to compute the exact maximum likelihood (ML) estimates, since the generalized least squares (GLS) estimates frequently imply a non-invertible process for the disturbance term, and, for identification purposes, it is necessary that the process be invertible. The article is directed at the situation arising when the GLS estimates do not satisfy invertibility whereas the ML estimates do, this lack of invertibility of the OLS estimates being brought about by the omission of the extra terms in the likelihood function

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http://hdl.handle.net/10453/13903