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Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models

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Author Info
Jurgen A. Doornik (Nuffield College, Oxford University)
Marius Ooms (Dept of Economics, Free University of Amsterdam)

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Abstract

We discuss computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models. We show how efficient computation and simulation is feasible, even for large samples. We also discuss the implementation of analytical bias corrections.

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Publisher Info
Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2001-W27.

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Length: 14 pages
Date of creation: 29 Nov 2001
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Handle: RePEc:nuf:econwp:0127

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Web page: http://www.nuff.ox.ac.uk/economics/

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Related research
Keywords: Long memory Bias Modified profile likelihood Restricted maximum likelihood estimator Time-series regression model likelihood

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

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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. 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. [Downloadable!] (restricted)
  2. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    Other versions:
  3. Smith, Anthony A, Jr & Sowell, Fallaw & Zin, Stanley E, 1997. "Fractional Integration with Drift: Estimation in Small Samples," Empirical Economics, Springer, vol. 22(1), pages 103-16.
    Other versions:
  4. M. Ooms & J.A. Doornik, 1999. "Inference and forecasting for fractional autoregressive integrated moving average models; with an application to US and UK inflation," Econometric Institute Report 171, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
  5. Beveridge, Steve & Oickle, Cyril, 1993. "Estimating fractionally integrated time series models," Economics Letters, Elsevier, vol. 43(2), pages 137-142. [Downloadable!] (restricted)
  6. Chung, Ching-Fan & Baillie, Richard T, 1993. "Small Sample Bias in Conditional Sum-of-Squares Estimators of Fractionally Integrated ARMA Models," Empirical Economics, Springer, vol. 18(4), pages 791-806.
  7. Bollerslev, Tim & Jubinski, Dan, 1999. "Equity Trading Volume and Volatility: Latent Information Arrivals and Common Long-Run Dependencies," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 9-21, January.
  8. Michael Dueker & Richard Startz, 1998. "Maximum-Likelihood Estimation Of Fractional Cointegration With An Application To U.S. And Canadian Bond Rates," The Review of Economics and Statistics, MIT Press, vol. 80(3), pages 420-426, August. [Downloadable!] (restricted)
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Cited by:
(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. Marius Ooms & M. Angeles Carnero & Siem Jan Koopman, 2004. "Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices," Econometric Society 2004 Australasian Meetings 158, Econometric Society. [Downloadable!]
    Other versions:
  2. Jurgen Doornik & Marius Ooms, 2004. "Inference and Forecasting for ARFIMA Models With an Application to US and UK Inflation," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 8(2), pages 1218-1218. [Downloadable!] (restricted)
  3. Sandrine Lardic & Valerie Mignon, 2004. "The exact maximum likelihood estimation of ARFIMA processes and model selection criteria: A Monte Carlo study," Economics Bulletin, Economics Bulletin, vol. 3(21), pages 1-16. [Downloadable!]
    Other versions:
  4. E. Dubois & S. Lardic & V. Mignon, 2003. "The exact maximum likelihood-based test for fractional cointegration: critical values, power and size," THEMA Working Papers 2003-26, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise. [Downloadable!]
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  5. Charles S. Bos, 2003. "Time Series Modelling using TSMod 3.24," Tinbergen Institute Discussion Papers 03-091/4, Tinbergen Institute. [Downloadable!]
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  6. Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute. [Downloadable!]
    Other versions:
  7. Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Impact of jumps on returns and realised variances: econometric analysis of time-deformed Levy processes," Economics Papers 2003-W12, Economics Group, Nuffield College, University of Oxford. [Downloadable!]
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  8. Siem Jan Koopman & Marius Ooms & M. Angeles Carnero, 2005. "Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 05-091/4, Tinbergen Institute. [Downloadable!]
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  9. Geetesh Bhardwaj & Norman Swanson, 2004. "An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series," Departmental Working Papers 200422, Rutgers University, Department of Economics. [Downloadable!]
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  10. Fulvio Corsi & Uta Kretschmer & Stefan Mittnik & Christian Pigorsch, 2005. "The Volatility of Realized Volatility," CFS Working Paper Series 2005/33, Center for Financial Studies. [Downloadable!]
  11. Charles S. Bos & Siem Jan Koopman & Marius Ooms, 2007. "Long memory modelling of inflation with stochastic variance and structural breaks," CREATES Research Papers 2007-44, School of Economics and Management, University of Aarhus. [Downloadable!]
    Other versions:
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