Theory and practice of multivariate arma forecasting

Trond Riise · Dag Tjozstheim

41 citations0/0 files storedJournal of Forecasting

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Forecasting Techniques and Applications Time Series Analysis and Forecasting Statistical and numerical algorithms

Abstract

Abstract We compare univariate and multivariate forecasts based on ARMA models. In theory we cannot do worse by using a multivariate model instead of a univariate one, but we can risk getting no improvement. Conditions for no improvements are discussed as well as cases where large improvements occur. The effect of estimated parameters is examined and found to be small granted that a good method of estimation is used. However, multivariate models could be very sensitive to structural changes. This is illustrated via an example involving monetary data, where the multivariate forecasts perform considerably worse than the univariate ones. This seems to put a limitation on the use of multivariate ARMA forecasting models.

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References

1969Investigating Causal Relations by Econometric Models and Cross-spectral Methods 1972MONEY, INCOME, AND CAUSALITY 2000Forecasting Economic Time Series 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition 1977Forecasting Economic Time Series 1979Accuracy of Forecasting: An Empirical Investigation 1977Relationships—and the Lack Thereof—Between Economic Time Series, with Special Reference to Money and Interest Rates 1977Relationships--and the Lack Thereof--Between Economic Time Series, with Special Reference to Money and Interest Rates

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