Adenomon, M.O. and Oyejola, B.A. (2014) Forecasting Performances of the Reduced Form VAR and Sims-Zha Bayesian VAR Models When the Multiple Times Series Are Jointly Influence by Collinearity and Autocorrelation. A Paper Presented at the 1st Int’l Conference on Postgraduate Students’ Association, University of Ilorin, Ilorin, Kwara State, Nigeria, 14-16 October 2014.

has been cited by the following article:

• TITLE: On the Performances of Classical VAR and Sims-Zha Bayesian VAR Models in the Presence of Collinearity and Autocorrelated Error Terms

  AUTHORS: M. O. Adenomon, V. A. Michael, O. P. Evans

  KEYWORDS: Vector Autoregression (VAR), Classical VAR, Bayesian VAR (BVAR), Sims-Zha Prior, Collinearity, Autocorrelation

  JOURNAL NAME: Open Journal of Statistics, Vol.6 No.1, February 25, 2016

  ABSTRACT: In time series literature, many authors have found out that multicollinearity and autocorrelation usually afflict time series data. In this paper, we compare the performances of classical VAR and Sims-Zha Bayesian VAR models with quadratic decay on bivariate time series data jointly influenced by collinearity and autocorrelation. We simulate bivariate time series data for different collinearity levels (﹣0.99, ﹣0.95, ﹣0.9, ﹣0.85, ﹣0.8, 0.8, 0.85, 0.9, 0.95, 0.99) and autocorrelation levels (﹣0.99, ﹣0.95, ﹣0.9, ﹣0.85, ﹣0.8, 0.8, 0.85, 0.9, 0.95, 0.99) for time series length of 8, 16, 32, 64, 128, 256 respectively. The results from 10,000 simulations reveal that the models performance varies with the collinearity and autocorrelation levels, and with the time series lengths. In addition, the results reveal that the BVAR4 model is a viable model for forecasting. Therefore, we recommend that the levels of collinearity and autocorrelation, and the time series length should be considered in using an appropriate model for forecasting.