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 InfoPaper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-670.
Date of creation: Sep 2009
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Other versions of this item:
- Shiqing Ling & Michael McAleer, 2010. "A general asymptotic theory for time-series models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(1), pages 97-111.
- NEP-ALL-2009-09-26 (All new papers)
- NEP-ECM-2009-09-26 (Econometrics)
- NEP-ETS-2009-09-26 (Econometric Time Series)
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- 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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