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

Peter M Robinson

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: 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.

Keywords: Long memory; conditional-sum-of-squares estimation; central limit theorem; almost sure convergence. (search for similar items in EconPapers)
Date: 2006-09
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:505

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