The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models

Seasonal Autoregressive Fractionally Integrated Moving Average SARFIMA models are used in the analysis of seasonal long memory-dependent time series. Two methods, which are conditional sum of squares CSS and two-staged methods introduced by Hosking 1984 , are proposed to estimate the parameters of SARFIMA models. However, no simulation study has been conducted in the literature. Therefore, it is not known how these methods behave under different parameter settings and sample sizes in SARFIMA models. The aim of this study is to show the behavior of these methods by a simulation study. According to results of the simulation, advantages and disadvantages of both methods under different parameter settings and sample sizes are discussed by comparing the root mean square error RMSE obtained by the CSS and two-staged methods. As a result of the comparison, it is seen that CSS method produces better results than those obtained from the two-staged method.