Semiparametric Estimation in Time Series Regression with Long Range Dependence
AbstractWe 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 neighborhood of the origin, and only periodogram ordinates in a degenerating band around the origin are used. This setting differs from earlier work 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 exists, which has the same asymptotic properties as the infeasible estimate. By Monte Carlo simulation, we evaluate the finite-sample performance of the generalized least squares estimate and the feasible estimate.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series Economics Working Papers with number 2002-17.
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Fractional integration; generalized least squares; linear regression; long range dependence; semiparametric estimation; Whittle likelihood;
Other versions of this item:
- Morten Orregaard Nielsen, 2005. "Semiparametric Estimation in Time-Series Regression with Long-Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 279-304, 03.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-03-28 (All new papers)
- NEP-ECM-2004-03-28 (Econometrics)
- NEP-ETS-2004-03-28 (Econometric Time Series)
- NEP-RMG-2004-03-28 (Risk Management)
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