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The Correlogram of a Long Memory Process Plus a Simple Noise

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Author Info
Clive W.J. Granger
Francesc Marmol

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Abstract

A frequent property of data, particularly in the financial area, is that the correlogram is low but remains positive for many lags. A plausible explanation for this is that the process consists of a stationary, long memory component plus a white noise component of much larger variance. The implications of such a composition are explored including the consequences for estimation of the long memory parameter.

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Publisher Info
Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number 97-29.

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Date of creation: Nov 1997
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Handle: RePEc:cdl:ucsdec:97-29

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  1. Hosking, Jonathan R. M., 1996. "Asymptotic distributions of the sample mean, autocovariances, and autocorrelations of long-memory time series," Journal of Econometrics, Elsevier, vol. 73(1), pages 261-284, July. [Downloadable!] (restricted)
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Yixiao Sun & Peter C.B. Phillips, 2002. "Nonlinear Log-Periodogram Regression for Perturbed Fractional Processes," Cowles Foundation Discussion Papers 1366, Cowles Foundation, Yale University. [Downloadable!]
    Other versions:
  2. Clive W.J. Granger & Namwon Hyung, 1999. "Occasional Structural Breaks and Long Memory," University of California at San Diego, Economics Working Paper Series 99-14, Department of Economics, UC San Diego. [Downloadable!]
    Other versions:
  3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," Center for Financial Institutions Working Papers 99-08, Wharton School Center for Financial Institutions, University of Pennsylvania. [Downloadable!]
    Other versions:
  4. Miguel Arranz & Francesc Marmol, 2001. "Out-of-sample forecast errors in misspecific perturbed long memory processes," Statistical Papers, Springer, vol. 42(4), pages 423-436, October. [Downloadable!] (restricted)
  5. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2001. "Outliers And Conditional Autoregressive Heteroscedasticity In Time Series," Statistics and Econometrics Working Papers ws010704, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
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