Estimation of the Memory Parameters of the Fractionally Integrated Separable Spatial Autoregressive (FISSAR(1, 1)) Model: A Simulation Study

• Published in: Communications in statistics. Simulation and computation Vol. 38; no. 6; pp. 1256 - 1268
• Main Authors: Ghodsi, Alireza, Shitan, Mahendran
• Format: Journal Article
• Language: English
• Published: Colchester Taylor & Francis Group 14.05.2009; Taylor & Francis
• Subjects: Autoregressive process; Exact sciences and technology; FISSAR; Fractional integration; Linear inference, regression; Mathematics; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Probability and statistics; Sciences and techniques of general use; Separable process; Spatial model; Statistics; Whittle's estimation; Subject Classification 62M30; Parameter estimation; Error estimation; Bias; Autoregressive model; FISSAR; Whittle's estimation; Statistical simulation; Implementation; Mean square error; Separable process; Autoregressive process; Simulation model; Spatial model; Method study; Fractional integration; Statistical estimation; Mean value; Mean estimation; Statistical method; Statistical regression; Numerical simulation; Mean error; Application; Biased estimation; Autoregressive processes
• ISSN: 0361-0918; 1532-4141
• DOI: 10.1080/03610910902898465

In this article, we implement the Regression Method for estimating (d 1 , d 2 ) of the FISSAR(1, 1) model. It is also possible to estimate d 1 and d 2 by Whittle's method. We also compute the estimated bias, standard error, and root mean square error by a simulation study. A comparison was made between the Regression Method of estimating d 1 and d 2 to that of the Whittle's method. It was found in this simulation study that the Regression Method of estimation was better when compare with the Whittle's estimator, in the sense that it had smaller root mean square errors (RMSE) values.