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
Full text not available from this repository.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 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 |
|---|---|
| Official URL: | http://eu.wiley.com/WileyCDA/WileyTitle/productCd-... |
| Additional Information: | © 2002 The Econometric Society |
| Divisions: | Economics STICERD |
| 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: | 13 Sep 2024 21:29 |
| URI: | http://eprints.lse.ac.uk/id/eprint/1290 |
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