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.
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