Univariate and Multivariate Arima versus Vector Autoregression Forecasting
The purposes of this study are two: 1) to compare the forecasting abilities of the three methods: univariate autoregressive integrated moving average (ARIMA), multivariate autoregressive integrated moving average (MARIMA), and vector autoregression (both unconstrained-VAR-and Bayesian-BVAR and 2) to study the idea that one advantage of vector autoregressions is that the models can easily and inexpensively be reestimated after each additional data point. All of these methods have been shown to provide forecasts that are more accurate than many econometric methods, which require more resources to implement.
Suggested citation: Bagshaw, Michael L., 1987. “Univariate and Multivariate Arima versus Vector Autoregression Forecasting,” Federal Reserve Bank of Cleveland, Working Paper no. 87-06.