Vector Autoregressive Model Using Non-Normal Copula Errors

Author(s)	Beulah S William, Nimitha John
Country	India
Abstract	This article focuses on a bivariate vector autoregressive model of lag 1 with non-normal errors. We propose a bivariate error distribution using two identical marginals through a copula function. The copula used for the study is the Farlie-Gumbel-Morgenstern copula, which is considered to be one of the efficient class of copula in describing the dependence between two random variables. The model parameters are estimated using the method of inference functions for margins, and the finite sample properties of the model is illustrated through simulation studies. A real life example is considered to illustrate the applications of the proposed model. The adequacy of the copula model is examined using the two sample version of kolmogorov smirnov test statistic.
Keywords	Vector autoregression, Copula Function, FGM copula, Inference Function for margins.
Field	Mathematics > Statistics
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-05-19
Cite This	Vector Autoregressive Model Using Non-Normal Copula Errors - Beulah S William, Nimitha John - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.20508
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.20508
Short DOI	https://doi.org/gtvtx7

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research