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Working Paper

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.

Working Papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to stimulate discussion and critical comment on research in progress. They may not have been subject to the formal editorial review accorded official Federal Reserve Bank of Cleveland publications. The views expressed in this paper are those of the authors and do not represent the views of the Federal Reserve Bank of Cleveland or the Federal Reserve System.


Suggested Citation

Bagshaw, Michael L. 1987. “Univariate and Multivariate Arima versus Vector Autoregression Forecasting.” Federal Reserve Bank of Cleveland, Working Paper No. 87-06.