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Note on a new Seasonal Fractionally Integrated Separable Spatial Autoregressive Model

Author

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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)

  • Abdou Kâ Diongue

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

  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

In this paper, we introduce a new model called Fractionally Integrated Separable Spatial Autoregressive processes with Seasonality and denoted Seasonal FISSAR for two-dimensional spatial data. We focus on the class of separable spatial models whose correlation structure can be expressed as a product of correlations. This new modelling allows taking into account the seasonality patterns observed in spatial data. We investigate the properties of this new model providing stationary conditions, some explicit expressions form of the autocovariance function and the spectral density function. We establish the asymptotic behaviour of the spectral density function near the seasonal frequencies and perform some simulations to illustrate the behaviour of the model.

Suggested Citation

  • Papa Ousmane Cissé & Abdou Kâ Diongue & Dominique Guegan, 2016. "Note on a new Seasonal Fractionally Integrated Separable Spatial Autoregressive Model," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01278126, HAL.
    • Handle: RePEc:hal:cesptp:halshs-01278126
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01278126
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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. Thomas De Graaff & Raymond J.C.M. Florax & Peter Nijkamp & Aura Reggiani, 2001. "A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity," Journal of Regional Science, Wiley Blackwell, vol. 41(2), pages 255-276, May.
    3. Jurate saltyte Benth & Fred Espen Benth & Paulius Jalinskas, 2007. "A Spatial-temporal Model for Temperature with Seasonal Variance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(7), pages 823-841.
    4. 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).
    5. Martin Benirschka & James K. Binkley, 1994. "Land Price Volatility in a Geographically Dispersed Market," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 76(2), pages 185-195.
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    Cited by:

    1. Papa Ousmane Cissé & Dominique Guegan & Abdou Kâ Diongue, 2018. "On the parameters estimation of the Seasonal FISSAR Model," Post-Print halshs-01832115, HAL.
    2. Robinson, Peter, 2019. "Spatial long memory," LSE Research Online Documents on Economics 102182, London School of Economics and Political Science, LSE Library.

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    More about this item

    Keywords

    seasonality; spatial short memory; seasonal long memory; two-dimensional data; separable process; spatial stationary process; spatial autocovariance;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • 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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