Estimating and forecasting the long-memory parameter in the presence of periodicity
We consider one parametric and five semiparametric approaches to estimate D in SARFIMA (0, D, 0) s processes, that is, when the process is a fractionally integrated ARMA model with seasonality s. We also consider h-step-ahead forecasting for these processes. We present the proof of some features of this model and also a study based on a Monte Carlo simulation for different sample sizes and different seasonal periods. We compare the different estimation procedures analyzing the bias, the mean squared error values, and the confidence intervals for the estimators. We also consider three different methods to choose the total number of regressors in the regression analysis for the semiparametric class of estimation procedures. We apply the methodology to the Nile River flow monthly data, and also to a simulated seasonal fractionally integrated time series. Copyright © 2007 John Wiley & Sons, Ltd.
Volume (Year): 26 (2007)
Issue (Month): 6 ()
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References listed on IDEAS
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- Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
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- Ooms, M., 1995. "Flexible Seasonal Long Memory and Economic Time Series," Econometric Institute Research Papers EI 9515-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute. Full references (including those not matched with items on IDEAS)
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