A Simulation Study of Effects of Collinearity on Forecasting of Bivariate Time Series Data
Journal Title: Scholars Journal of Physics, Mathematics and Statistics - Year 2014, Vol 1, Issue 1
Abstract
Vector Autoregression (VAR) and Bayesian VAR (BVAR) were first introduced in the early eighties of the last century and have since proven to be practical and effective economic forecasting methodologies. This paper tends to study the forecasting performances of the unrestricted VAR model and four versions of the Sims-Zha BVAR models in the presence of collinearity using Monte Carlo simulation technique. We considered ten (10) collinearity levels (0.8, -0.8, 0.85, -0.85, 0.9, -0.9, 0.95, -0.95, 0.99 and -0.99), the results from our simulation study revealed that the forecasting performances of the models vary as the collinearity levels varied. Furthermore, the values of the criteria increases as the time series length and the collinearity levels increased. We therefore recommend that if VAR modelers know that collinearity is acting upon the model, one can choose the forecasting model that is preferred for the criteria and the time series length selected.
Authors and Affiliations
Adenomon M. O, Oyejola B. A.
Keywords
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