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Conditional-Sum-of-Squares Estimation ofModels for Stationary Time Series with Long Memory


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  • Peter M Robinson
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    Employing 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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    Paper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2006/505.

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    Date of creation: Sep 2006
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    Handle: RePEc:cep:stiecm:/2006/505

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    Keywords: Long memory; conditional-sum-of-squares estimation; central limit theorem; almost sure convergence.;

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    1. 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.
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    Cited by:
    1. 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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