Estimation in nonlinear time series models

A general framework for analyzing estimates in nonlinear time series is developed. General conditions for strong consistency and asymptotic normality are derived both for conditional least squares and maximum likelihood types estimates. Ergodie strictly stationary processes are studied in the first part and certain nonstationary processes in the last part of the paper. Examples are taken from most of the usual classes of nonlinear time series models.

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Year of publication:	1986
Authors:	Tjøstheim, Dag
Published in:	Stochastic Processes and their Applications. - Elsevier, ISSN 0304-4149. - Vol. 21.1986, 2, p. 251-273
Publisher:	Elsevier
Keywords:	asymptotic normality conditional least square consistency maximum likelihood nonlinear time series

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008875056