Cointegration models with non Gaussian GARCH innovations

作     者：John, Nimitha Narayana, Balakrishna 

作者机构：Cochin Univ Sci & Technol Dept Statis Cochin 682022 Kerala India 

出 版 物：《METRON-INTERNATIONAL JOURNAL OF STATISTICS》 (密特隆)

年 卷 期：2018年第76卷第1期

页      面：83-98页

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：Kerala State Council for Science Technology and Environment [1413/2012/KCSTE] DST SERB [SR/S4/MS:837/13] 

主　　题：Cointegration Fisher scoring algorithm Generalised autoregressive conditional heterosedasticity Volatility Models 

摘      要：This paper presents the estimation procedures for a bivariate cointegration model when the errors are generated by a constant conditional correlation model. In particular, the method of maximum likelihood is discussed when the errors follow Generalised Autoregressive Conditional Hetroskedastic (GARCH) models with Gaussian and some non Gaussian innovations. The method of estimation is illustrated using simulated observations. Data analysis is provided to highlight the applications of the proposed models.