Bayesian Vector Autoregression (BVAR) modeling and forecasting of the dynamic interrelationship between GDP and agricultural sector in Nigeria
Abstract
In the Bayesian VAr literatures, the Litterman Prior has been compared with other priors for example with Sims-Zha prior. It has been shown that the Sims-Zha prior has more advantages over the Litterman prior. For example, the Litterman prior estimates the VAr coefficients on equation-by-equation basis, but the Sims-Zha prior estimates the parameter for the full system in a multivariate regression. The implication is that the Sims-Zha prior allows for a more general specification and produce a tractable multivariate normal posterior distribution. We proposed four (4) versions of BVAr models of the secondary data on GDP and Agriculture sector for the Nigerian economy collected from CBN website from 1960 to 2011. We found that the BVAr1 produced the forecast with the minimum rmse and MAE as 0.05666357 and 0.03721166 respectively. We therefore concluded that from the economic point of view, our results suggested that in the presence of prior information (which can come from different sources, either experience or economy theory) can significantly improved the forecasts from economic models.
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References
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