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Forecasting Using Contemporaneous Correlations

In this paper, we present a forecasting technique that uses contemporaneous correlations for forecasting in a time series model when only a subset of the variables are available for the current period. This method potentially provides more accurate forecasts than the standard time series forecasting method, which does not use contemporaneous data. This procedure is illustrated with an example of forecasting the gross national product (GNP), given current N-i in a trivariate autoregressive moving average time series model, Results indicate that during the more stable economic period of 1976:IQ through 1979:IVQ, this method indeed provides forecasts with smaller root mean square errors than the standard forecasts. However, the results during the more turbulent i980s are mixed. This latter result indicates that the relationship between the contemporaneous error terms from N-i and GNP changed during this period. However, the results for the period i983:IIQ through 1984:IIQ indicate that the relationship may have returned to pre-1980 form. the forecast errors during this latter period had smaller root mean square errors when the contemporaneous errors were used.

Keywords: contemporaneous correlations, forecasting, multivariate time series

Suggested citation: Bagshaw, Michael L., 1984. “Forecasting Using Contemporaneous Correlations,” Federal Reserve Bank of Cleveland, Working Paper no. 84-03.

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