Conditional-Sum-of-Squares Estimation ofModels for Stationary Time Series with Long Memory
AbstractEmploying recent results of Robinson (2005) we consider the asymptotic properties ofconditional-sum-of-squares (CSS) estimates of parametric models for stationary timeseries with long memory. CSS estimation has been considered as a rival to Gaussianmaximum likelihood and Whittle estimation of time series models. The latter kinds ofestimate have been rigorously shown to be asymptotically normally distributed in case oflong memory. However, CSS estimates, which should have the same asymptoticdistributional properties under similar conditions, have not received comparabletreatment: the truncation of the infinite autoregressive representation inherent in CSSestimation has been essentially ignored in proofs of asymptotic normality. Unlike in shortmemory models it is not straightforward to show the truncation has negligible effect.
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Bibliographic InfoPaper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2006/505.
Date of creation: Sep 2006
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Web page: http://sticerd.lse.ac.uk/_new/publications/default.asp
Long memory; conditional-sum-of-squares estimation; central limit theorem; almost sure convergence.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-10-28 (All new papers)
- NEP-ECM-2006-10-28 (Econometrics)
- NEP-ETS-2006-10-28 (Econometric Time Series)
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.:
- Peter M Robinson, 2004. "Efficiency Improvements in Inference on Stationary and Nonstationary Fractional Time Series," STICERD - Econometrics Paper Series /2004/480, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Baillie, Richard T. & Kapetanios, George, 2008. "Nonlinear models for strongly dependent processes with financial applications," Journal of Econometrics, Elsevier, vol. 147(1), pages 60-71, November.
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