FORECASTING THE BUSINESS-CYCLE WITHOUT USING MINIMUM AUTOCORRELATION FACTORS

We introduce a forecasting technique based on multivariate ideas previ ously applied in remote sensing. The approach has the trivial but none theless fundamental purpose of dividing the information inherent in th e time series into important and unimportant. Important information is used for forecasting purposes while the unimportant is discarded. Alt hough related to vector autoregression, giving asymptotically the same estimates, there are reasons to believe that the approach gives bette r precision of parameter estimates for finite samples as well as more precise predictions.