In this paper, the use of weighted pairwise likelihood instead of the full likelihood in estimating the parameters of the multivariate AR(1) is investigated. A closed formula for typical elements of the Godambe information (sandwich information) is presented. Some efficiency calculations are also given to discuss the feasibility and computational advantages of the weighted pairwise likelihood approach relative to the full likelihood approach.
M. R., K., & Nematollahi, A. R. (2012). A weighted pairwise likelihood approach to multivariate AR(1) models. Iranian Journal of Science, 36(2), 137-145. doi: 10.22099/ijsts.2012.2064
MLA
Kazemi M. R.; A. R. Nematollahi. "A weighted pairwise likelihood approach to multivariate AR(1) models". Iranian Journal of Science, 36, 2, 2012, 137-145. doi: 10.22099/ijsts.2012.2064
HARVARD
M. R., K., Nematollahi, A. R. (2012). 'A weighted pairwise likelihood approach to multivariate AR(1) models', Iranian Journal of Science, 36(2), pp. 137-145. doi: 10.22099/ijsts.2012.2064
VANCOUVER
M. R., K., Nematollahi, A. R. A weighted pairwise likelihood approach to multivariate AR(1) models. Iranian Journal of Science, 2012; 36(2): 137-145. doi: 10.22099/ijsts.2012.2064
)