A general asymptotic theory for time-series models

Swaaj Ling, Michael McAleer

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Scopus)

Abstract

This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodic time-series models. Under simple conditions that are straightforward to check, we establish the strong consistency, the rate of strong convergence and the asymptotic normality of a general class of estimators that includes LSE, MLE and some M-type estimators. As an application, we verify the assumptions for the long-memory fractional ARIMA model. Other examples include the GARCH(1,1) model, random coefficient AR(1) model and the threshold MA(1) model.
Original languageEnglish
Pages (from-to)97-111
Number of pages15
JournalStatistica Neerlandica
Volume64
Issue number1
DOIs
Publication statusPublished - 2010

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