Some identification and estimation results for regression models with stochastically varying parametres

Pagan, AR

Some identification and estimation results for regression models with stochastically varying parametres

Pagan, AR

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Publisher:: Elsevier Science Publishers B.V.
Publication Type:: Journal Article
Citation:: Journal of Econometrics, 1980, 13 (3), pp. 341 - 363
Issue Date:: 1980-01

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Field	Value	Language
dc.contributor.author	Pagan, AR	en_US
dc.date.issued	1980-01	en_US
dc.identifier.citation	Journal of Econometrics, 1980, 13 (3), pp. 341 - 363	en_US
dc.identifier.issn	0304-4076	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13916
dc.description.abstract	Although various theoretical and applied papers have appeared in recent years concerned with the estimation and use of regression models with stochastically varying coefficients, little is available in the literature on the properties of the proposed estimators or the identifiability of the parameters of such models. The present paper derives sufficient conditions under which the maximum likelihood estimator is consistent and asymptotically normal and also provides sufficient conditions for the estimation of regression models with stationary stochastically varying coefficients. In many instances these requirements are found to have simple, intuitively appealing interpretations. Consistency and asymptotic normality is also proven for a two-step estimator and a method suggested by Rosenberg for generating initial estimates.	en_US
dc.publisher	Elsevier Science Publishers B.V.	en_US
dc.relation.ispartof	Journal of Econometrics	en_US
dc.relation.isbasedon	10.1016/0304-4076(80)90084-6	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Some identification and estimation results for regression models with stochastically varying parametres	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	13	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1402 Applied 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	3	en_US
pubs.volume	13	en_US

Abstract:

Although various theoretical and applied papers have appeared in recent years concerned with the estimation and use of regression models with stochastically varying coefficients, little is available in the literature on the properties of the proposed estimators or the identifiability of the parameters of such models. The present paper derives sufficient conditions under which the maximum likelihood estimator is consistent and asymptotically normal and also provides sufficient conditions for the estimation of regression models with stationary stochastically varying coefficients. In many instances these requirements are found to have simple, intuitively appealing interpretations. Consistency and asymptotic normality is also proven for a two-step estimator and a method suggested by Rosenberg for generating initial estimates.

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http://hdl.handle.net/10453/13916