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has been cited by the following article:

• TITLE: Short Term Forecasting Performances of Classical VAR and Sims-Zha Bayesian VAR Models for Time Series with Collinear Variables and Correlated Error Terms

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

  KEYWORDS: Short term, Forecasting, Vector Autoregressive (VAR), Bayesian VAR (BVAR), Sims-Zha Prior, Collinearity, Error Terms

  JOURNAL NAME: Open Journal of Statistics, Vol.5 No.7, December 18, 2015

  ABSTRACT: Forecasts can either be short term, medium term or long term. In this work we considered short term forecast because of the problem of limited data or time series data that is often encounter in time series analysis. This simulation study considered the performances of the classical VAR and Sims-Zha Bayesian VAR for short term series at different levels of collinearity and correlated error terms. The results from 10,000 iteration revealed that the BVAR models are excellent for time series length of T=8 for all levels of collinearity while the classical VAR is effective for time series length of T=16 for all collinearity levels except when ρ = -0.9 and ρ = -0.95. We therefore recommended that for effective short term forecasting, the time series length, forecasting horizon and the collinearity level should be considered.