A general asymptotic theory for time-series models
AbstractThis 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.
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Bibliographic InfoArticle provided by Netherlands Society for Statistics and Operations Research in its journal Statistica Neerlandica.
Volume (Year): 64 (2010)
Issue (Month): 1 ()
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402
Other versions of this item:
- Shiqing Ling & Michael McAleer, 2009. "A General Asymptotic Theory for Time Series Models," CIRJE F-Series CIRJE-F-670, CIRJE, Faculty of Economics, University of Tokyo.
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- Christian Francq & Jean-Michel ZakoÃ¯an, 2013.
"Estimating the Marginal Law of a Time Series With Applications to Heavy-Tailed Distributions,"
Journal of Business & Economic Statistics,
Taylor & Francis Journals, vol. 31(4), pages 412-425, October.
- Christian Francq & Jean-Michel Zakoïan, 2011. "Estimating the Marginal Law of a Time Series with Applications to Heavy Tailed Distributions," Working Papers 2011-30, Centre de Recherche en Economie et Statistique.
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