Journal
STATISTICS
Volume 50, Issue 2, Pages 298-311Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/02331888.2015.1086769
Keywords
seasonal fractional ARIMA; stable distributions; ECF estimate; whittle estimate; Markov Chains Monte Carlo; Two-Step method
Categories
Funding
- CEA-MITIC, an African Center of Excellence in Mathematics, Informatics and ICT
Ask authors/readers for more resources
Seasonal fractional ARIMA (ARFISMA) model with infinite variance innovations is used in the analysis of seasonal long-memory time series with large fluctuations (heavy-tailed distributions). Two methods, which are the empirical characteristic function (ECF) procedure developed by Knight and Yu [The empirical characteristic function in time series estimation. Econometric Theory. 2002; 18: 691-721] and the Two-Step method (TSM) are proposed to estimate the parameters of stable ARFISMA model. The ECF method estimates simultaneously all the parameters, while the TSM considers in the first step the Markov Chains Monte Carlo-Whittle approach introduced by Ndongo et al. [Estimation of long-memory parameters for seasonal fractional ARIMA with stable innovations. Stat Methodol. 2010;7:141-151], combined with the maximum likelihood estimation method developed by Alvarez and Olivares [Methodes d'estimation pour des lois stables avec des applications en finance. Journal de la Societe Francaise de Statistique. 2005;1(4):23-54] in the second step. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available