Adapting to unknown disturbance autocorrelation in regression with long memory

Javier Hidalgo and Peter Robinson

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: 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.

Keywords: Time series regression; long memory; adaptive estimation (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2001-09
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:2078

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