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.
Highlights
There are various objectives for studying time series
Adenomon and Oyejola [5] studied the performances of Vector Autoregression (VAR) and Bayesian VAR (BVAR) model when the bivariate time series were jointly influenced by collinearity and autocorrelation
This work examines the performances of classical VAR and Sims-Zha Bayesian VAR in the presence of collinearity and autocorrelation
Summary
There are various objectives for studying time series. These include the understanding and description of the generated mechanism, the forecasting of future value and optimum control of a system [1]. Gujarati [2] observed that multicollinearity problem usually afflicted the VAR models. In a recent work of Garba et al [4], they observed that the autocorrelation problem usually afflicted time series data. Adenomon and Oyejola [5] studied the performances of VAR and BVAR model (assuming harmonic decay) when the bivariate time series were jointly influenced by collinearity and autocorrelation
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