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Adapting to unknown disturbance autocorrelation in regression with long memory

Hidalgo, Javier and Robinson, Peter (2001) Adapting to unknown disturbance autocorrelation in regression with long memory. Econometrics; EM/2001/427 (EM/01/427). Suntory and Toyota International Centres for Economics and Related Disciplines, London, UK.

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We show that it is possible to adapt to nonparametric disturbance autocorrelation in time series regression in the presence of long memory in both regressors and disturbances by using a smoothed nonparametric spectrum estimate in frequency-domain generalized least squares. When the collective memory in regressors and disturbances is sufficiently strong, ordinary least squares is not only asymptotically inefficient but asymptotically non-normal and has a slow rate of convergence, whereas generalized least squares is asymptotically normal and Gauss-Markov efficient with standard convergence rate. Despite the anomalous behaviour of nonparametric spectrum estimates near a spectral pole, we are able to justify a standard construction of frequency-domain generalized least squares, earlier considered in case of short memory disturbances. A small Monte Carlo study of finite sample performance is included.

Item Type: Monograph (Discussion Paper)
Official URL:
Additional Information: © 2001 the authors
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C22 - Time-Series Models
Date Deposited: 27 Apr 2007
Last Modified: 15 Sep 2023 22:49

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