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On the parameters estimation of the Seasonal FISSAR Model

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  • Papa Ousmane Cissé

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, LERSTAD - laboratoire d'Etudes et de recherches en Statistiques et Développement - UGB - Université Gaston Berger de Saint-Louis Sénégal, LMM - Laboratoire Manceau de Mathématiques - UM - Le Mans Université)

  • Dominique Guegan

    (UP1 - Université Paris 1 Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne, University of Ca’ Foscari [Venice, Italy])

  • Abdou Kâ Diongue

    (LERSTAD - laboratoire d'Etudes et de recherches en Statistiques et Développement - UGB - Université Gaston Berger de Saint-Louis Sénégal)

Abstract

In this paper, we discuss the methods of estimating the parameters of the Seasonal FISSAR (Fractionally Integrated Separable Spatial Autoregressive with seasonality) model. First we implement the regression method based on the log-periodogram and the classical Whittle method for estimating memory parameters. To estimate the model's parameters simultaneously - innovation parameters and memory parameters- the maximum likelihood method, and the Whittle method based on the MCMC simulation are considered. We are investigated the consistency and the asymptotic normality of the estimators by simulation.

Suggested Citation

  • Papa Ousmane Cissé & Dominique Guegan & Abdou Kâ Diongue, 2018. "On the parameters estimation of the Seasonal FISSAR Model," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01832115, HAL.
    • Handle: RePEc:hal:cesptp:halshs-01832115
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01832115
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    References listed on IDEAS

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    1. Roger J. Marshall, 1991. "A Review of Methods for the Statistical Analysis of Spatial Patterns of Disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(3), pages 421-441, May.
    2. Papa Ousmane Cissé & Abdou Kâ Diongue & Dominique Guegan, 2016. "Statistical properties of the seasonal fractionally integrated separable spatial autoregressive model," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01397357, HAL.
    3. Papa Ousmane Cissé & Abdou Kâ Diongue & Dominique Guegan, 2016. "Note on a new Seasonal Fractionally Integrated Separable Spatial Autoregressive Model," Documents de travail du Centre d'Economie de la Sorbonne 16013, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    4. Diongue, Abdou Kâ & Diop, Aliou & Ndongo, Mor, 2008. "Seasonal fractional ARIMA with stable innovations," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1404-1411, September.
    5. Lambert, Dayton M. & Lowenberg-DeBoer, James & Bongiovanni, Rodolfo, 2003. "Spatial Regression Models For Yield Monitor Data: A Case Study From Argentina," 2003 Annual meeting, July 27-30, Montreal, Canada 22022, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Klüppelberg, Claudia & Mikosch, Thomas, 1993. "Spectral estimates and stable processes," Stochastic Processes and their Applications, Elsevier, vol. 47(2), pages 323-344, September.
    7. John Geweke & Susan Porter‐Hudak, 1983. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 221-238, July.
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    More about this item

    Keywords

    Seasonal FISSAR; long memory; regression method; Whittle method; MLE method;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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