Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory
AbstractWe show that it is possible to adapt to nonparametric disturbance auto-correlation 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.
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Bibliographic InfoPaper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2001/427.
Date of creation: Sep 2001
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Time series regression; long memory; adaptive estimation.;
Other versions of this item:
- Javier Hidalgo & Peter M. Robinson, 2002. "Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory," Econometrica, Econometric Society, vol. 70(4), pages 1545-1581, July.
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- Robinson, Peter M, 1988. "The Stochastic Difference between Econometric Statistics," Econometrica, Econometric Society, vol. 56(3), pages 531-48, May.
- Hannan, E J & Terrell, R D, 1973. "Multiple Equation Systems with Stationary Errors," Econometrica, Econometric Society, vol. 41(2), pages 299-320, March.
- Hannan, E J & Terrell, R D, 1972. "Time Series Regression with Linear Constraints," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(2), pages 189-200, June.
- Robinson, P M, 1991. "Automatic Frequency Domain Inference on Semiparametric and Nonparametric Models," Econometrica, Econometric Society, vol. 59(5), pages 1329-63, September.
- Robinson, P M, 1976. "Instrumental Variables Estimation of Differential Equations," Econometrica, Econometric Society, vol. 44(4), pages 765-76, July.
- Hosoya, Yuzo, 1996. "The quasi-likelihood approach to statistical inference on multiple time-series with long-range dependence," Journal of Econometrics, Elsevier, vol. 73(1), pages 217-236, July.
- Xiao, Zhijie & Phillips, Peter C. B., 2002. "Higher order approximations for Wald statistics in time series regressions with integrated processes," Journal of Econometrics, Elsevier, vol. 108(1), pages 157-198, May.
- George Kapetanios & Zacharias Psaradakis, 2007. "Semiparametric Sieve-Type GLS Inference in Regressions with Long-Range Dependence," Working Papers 587, Queen Mary, University of London, School of Economics and Finance.
- Marcel Aloy & Gilles de Truchis, 2012.
"Estimation and Testing for Fractional Cointegration,"
AMSE Working Papers
1215, Aix-Marseille School of Economics, Marseille, France.
- Marcel Aloy & Gilles De Truchis, 2012. "Estimation and Testing for Fractional Cointegration," Working Papers halshs-00793206, HAL.
- Afonso Goncalves da Silva & Peter Robinson, 2008.
"Finite Sample Performance in Cointegration Analysis of Nonlinear Time Series with Long Memory,"
Taylor & Francis Journals, vol. 27(1-3), pages 268-297.
- Afonso Gonçalves da Silva & Peter M Robinson, 2006. "Finite Sample Performance in CointegrationAnalysis of Nonlinear Time Series with LongMemory," STICERD - Econometrics Paper Series /2006/501, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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