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On the Computation of Autocovariances for Generalized Gegenbauer Processes
On the Computation of Autocovariances for Generalized Gegenbauer Processes
RRS2011-06
Abstract
Gegenbauer processes and their generalizations represent an extremely general way of modeling long memory and seasonal long memory; they include ARFIMA, seasonal ARFIMA, and GARMA processes as special cases. Models from this class of processes have been used extensively in economics, finance and in the physical sciences and are thus of widespread interest. Nonetheless, one obstacle to using this class of models is finding explicit formulas for the autocovariances that are valid for all lags. We provide a computationally efficient method of computing these autocovariances by determining the moving average representation of these processes, and also give an asymptotic formula for the determinant of the covariance matrix. The techniques are then illustrated using maximum likelihood estimation to model atmospheric CO2 data.
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Statistical Research Reports and Studies
Page Last Revised - October 28, 2021
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