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:: Volume 16, Issue 2 (3-2012) ::
Andishe 2012, 16(2): 1-14 Back to browse issues page
Moving Average Processes with Infinite Variance
Mehrnaz Mohammadpour *, Fereshte Rezanezhad
Abstract:   (11031 Views)
The sample autocorrelation function (acf) of a stationary process has played a central statistical role in traditional time series analysis, where the assumption is made that the marginal distribution has a second moment. Now, the classical methods based on acf are not applicable in heavy tailed modeling. Using the codifference function as dependence measure for such processes be shown it be as a new tool for order identification of stable moving average processes. Based on the empirical characteristic function, we propose a consistent estimator of the codifference function. In addition, we derive the limiting distribution. Finally, simulation study shows the method is good.
Keywords: Symmetric Stable Distribution, Moving Average Processes, Order Identification, Codifference Function.
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Type of Study: Research | Subject: Special
Received: 2012/05/1 | Accepted: 2012/05/1 | Published: 2014/07/12
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Mohammadpour M, Rezanezhad F. Moving Average Processes with Infinite Variance. Andishe. 2012; 16 (2) :1-14
URL: http://andisheyeamari.irstat.ir/article-1-163-en.html


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Volume 16, Issue 2 (3-2012) Back to browse issues page
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