Asymptotic theory for certain regression models with long memory errors

Asymptotic theory for certain regression models with long memory errors

The asymptotic distribution of a weighted linear combination of a linear long memory series is shown to be normal for certain weights. This result can be used to derive the limiting distribution of the least squares estimators for polynomial trends and of the periodogram at fixed Fourier frequencies...

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Veröffentlicht in:Journal of time series analysis 1997-07, Vol.18 (4), p.385-393
1. Verfasser: Deo, R. S.
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Sprache:eng
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Long memory
Mathematical analysis
Regression analysis
regression models
Time series
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description The asymptotic distribution of a weighted linear combination of a linear long memory series is shown to be normal for certain weights. This result can be used to derive the limiting distribution of the least squares estimators for polynomial trends and of the periodogram at fixed Fourier frequencies. A closed form expression for the asymptotic relative bias of the tapered periodogram at fixed Fourier frequencies is also obtained. A weighted least squares estimator, which is asymptotically efficient for polynomial trend regressors, is shown to be asymptotically normal.
doi_str_mv 10.1111/1467-9892.00057
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subjects Long memory
Mathematical analysis
Regression analysis
regression models
Time series
title Asymptotic theory for certain regression models with long memory errors
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