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Semiparametric Estimation in Time-Series Regression with Long-Range Dependence

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  • Morten Orregaard Nielsen

Abstract

We consider semiparametric estimation in time-series regression in the presence of long-range dependence in both the errors and the stochastic regressors. A central limit theorem is established for a class of semiparametric frequency domain-weighted least squares estimates, which includes both narrow-band ordinary least squares and narrow-band generalized least squares as special cases. The estimates are semiparametric in the sense that focus is on the neighbourhood of the origin, and only periodogram ordinates in a degenerating band around the origin are used. This setting differs from earlier studies on time-series regression with long-range dependence, where a fully parametric approach has been employed. The generalized least squares estimate is infeasible when the degree of long-range dependence is unknown and must be estimated in an initial step. In that case, we show that a feasible estimate which has the same asymptotic properties as the infeasible estimate, exists. By Monte Carlo simulation, we evaluate the finite-sample performance of the generalized least squares estimate and the feasible estimate. Copyright 2005 Blackwell Publishing Ltd.

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

Volume (Year): 26 (2005)
Issue (Month): 2 (03)
Pages: 279-304

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Handle: RePEc:bla:jtsera:v:26:y:2005:i:2:p:279-304

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  1. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
  2. Hassler, U. & Marmol, Francesc & Velasco, Carlos, . "Residual log-periodogram inference for long-run relationships," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/4359, Universidad Carlos III de Madrid.
  3. Tsay, Wen-Jen & Chung, Ching-Fan, 2000. "The spurious regression of fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 96(1), pages 155-182, May.
  4. Velasco, Carlos, . "Gaussian semi-parametric estimation of fractional cointegration," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/4356, Universidad Carlos III de Madrid.
  5. Lobato, I. & Robinson, P. M., 1996. "Averaged periodogram estimation of long memory," Journal of Econometrics, Elsevier, vol. 73(1), pages 303-324, July.
  6. Lobato, Ignacio N., 1999. "A semiparametric two-step estimator in a multivariate long memory model," Journal of Econometrics, Elsevier, vol. 90(1), pages 129-153, May.
  7. Hannan, E. J., 1979. "The central limit theorem for time series regression," Stochastic Processes and their Applications, Elsevier, vol. 9(3), pages 281-289, December.
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Cited by:
  1. Afonso Goncalves da Silva & Peter Robinson, 2008. "Finite Sample Performance in Cointegration Analysis of Nonlinear Time Series with Long Memory," Econometric Reviews, Taylor and Francis Journals, vol. 27(1-3), pages 268-297.
  2. 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.
  3. Morten Ørregaard Nielsen & Per Frederiksen, 2008. "Fully Modified Narrow-Band Least Squares Estimation of Stationary Fractional Cointegration," Working Papers 1171, Queen's University, Department of Economics.
  4. Peter M Robinson, 2007. "Multiple Local Whittle Estimation in StationarySystems," STICERD - Econometrics Paper Series /2007/525, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

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