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.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ling, Shiqing & McAleer, Michael, 2003.
"Asymptotic Theory For A Vector Arma-Garch Model,"
Cambridge University Press, vol. 19(02), pages 280-310, April.
- Jeantheau, Thierry, 1998. "Strong Consistency Of Estimators For Multivariate Arch Models," Econometric Theory, Cambridge University Press, vol. 14(01), pages 70-86, February.
- J. Pfanzagl, 1969. "On the measurability and consistency of minimum contrast estimates," Metrika, Springer, vol. 14(1), pages 249-272, December.
- Christian Francq & Jean-Michel Zakoïan, 2011.
"Estimating the Marginal Law of a Time Series with Applications to Heavy Tailed Distributions,"
2011-30, Centre de Recherche en Economie et Statistique.
- 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.
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