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A Simulation Study on the Performances of Classical Var and sims-zha Bayesian Var Models in the Presence of Autocorrelated Errors

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Open Journal of Modelling and Simulation 2015年第4期3卷 146-158页

作者： M. O. Adenomon V. A. Michael O. P. Evans Department of Mathematics & Statistics The Federal Polytechnic Bida Nigeria

It is well known that a high degree of positive dependency among the errors generally leads to 1) serious underestimation of standard errors for regression coefficients;2) prediction intervals that are excessively wide. This paper set out to study the performances of classical VAR and sims-zha Bayesian VAR models in the presence of autocorrelated errors. Autocorrelation levels of (-0.99, -0.95, -0.9, -0.85, -0.8, 0.8, 0.85, 0.9, 0.95, 0.99) were considered for short term (T = 8, 16);medium term (T = 32, 64) and long term (T = 128, 256). The results from 10,000 simulation revealed that BVAR model with loose prior is suitable for negative autocorrelations and BVAR model with tight prior is suitable for positive autocorrelations in the short term. While for medium term, the BVAR model with loose prior is suitable for the autocorrelation levels considered except in few cases. Lastly, for long term, the classical VAR is suitable for all the autocorrelation levels considered except in some cases where the BVAR models are preferred. This work therefore concludes that the performance of the classical VAR and sims-zha Bayesian VAR varies in terms of the autocorrelation levels and the time series lengths.

关键词： Simulation Performances Vector Autoregression (VAR) Classical VAR sims-zha Prior Bayesian VAR (BVAR) Autocorrelated Errors

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Short Term Forecasting Performances of Classical VAR and sims-zha Bayesian VAR Models for Time Series with Collinear Variables and Correlated Error Terms

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Open Journal of Statistics 2015年第7期5卷 742-753页

作者： M. O. Adenomon V. A. Michael O. P. Evans Department of Mathematics & Statistics The Federal Polytechnic Bida Nigeria

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.

关键词： Short term Forecasting Vector Autoregressive (VAR) Bayesian VAR (BVAR) sims-zha Prior Collinearity Error Terms

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