ARFIMA; Exponential model; FEXP model; GARMA; k-factor GARMA; k-factor
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 effcient 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.
Tucker S. McElroy and Scott H. Holan. (2011). On the Computation of Autocovariances for Generalized Gegenbauer Processes. Center for Statistical Research & Methodology, Research and Methodology Directorate Research Report Series (Statistics #2011-06). U.S. Census Bureau. Available online at <http://www.census.gov/srd/papers/pdf/rrs2011-06.pdf>.
[PDF] or denotes a file in Adobe’s Portable Document Format. To view the file, you will need the Adobe® Reader®
available free from Adobe.
This symbol indicates a link to a non-government web site. Our linking to these sites does not constitute an endorsement of any products, services or the information found on them. Once you link to another site you are subject to the policies of the new site.