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Adapting to unknown disturbance autocorrelation in regression with long memory

Hidalgo, Javier and Robinson, Peter (2002) Adapting to unknown disturbance autocorrelation in regression with long memory. Econometrica, 70 (4). pp. 1545-1581. ISSN 0012-9682

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Identification Number: 10.1111/1468-0262.00341


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 behavior 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: Article
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Additional Information: © 2002 The Econometric Society
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods
C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C32 - Time-Series Models
Date Deposited: 27 Apr 2007
Last Modified: 03 Jan 2024 18:33

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