On the parameters estimation of the Seasonal FISSAR Model

Papa Cissé (), Dominique Guegan () and Abdou Ka Diongue
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Papa 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]

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

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.

Keywords: Seasonal FISSAR; long memory; regression method; Whittle method; MLE method (search for similar items in EconPapers)
Date: 2018-07
New Economics Papers: this item is included in nep-ecm
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01832115v1
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Published in 2018

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Related works:
Working Paper: On the parameters estimation of the Seasonal FISSAR Model (2018)
Working Paper: On parameters estimation of the Seasonal FISSAR Model (2018)
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