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Estimation of the fractionally integrated process with Missing Values: Simulation and Application


  • Valderio A. Reisen, UFES, Brazil.
  • Carlos Feitosa Luna
  • Manoel R. Sena Jr.


Time series with long-memory behavior have recently received much attention. Much interest attaches to parameter estimation in the ARFIMA model by considering different situations of this process, and specifically when there are missing observations. This is the focus of this paper. To estimate the parameters of the ARFIMA model, parametric and semiparametric approaches are considered. The way the missing values are distributed can affect the performance of these estimators. We consider two ways for the generating the missing observations: random and block. We also consider innovations that are not normally distributed. The results are obtained through Monte Carlo simulation and a real data set is used to illustrate the methodology

Suggested Citation

  • Valderio A. Reisen, UFES, Brazil. & Carlos Feitosa Luna & Manoel R. Sena Jr., 2004. "Estimation of the fractionally integrated process with Missing Values: Simulation and Application," Computing in Economics and Finance 2004 251, Society for Computational Economics.
  • Handle: RePEc:sce:scecf4:251

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    References listed on IDEAS

    1. Sweeney, Richard J., 1988. "Some New Filter Rule Tests: Methods and Results," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 23(03), pages 285-300, September.
    2. Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October.
    3. Ya-Chi Huang & Shu-Heng Chen, 2003. "Simulating the Evolution of Portfolio Behavior in a Multiple-Asset Agent-Based Artificial Stock Market," Computing in Economics and Finance 2003 62, Society for Computational Economics.
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    More about this item


    Long Memory; ARFIMA; Parametric and semi-parametric methods.;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection


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