Forecasting the business cycle without using minimum autocorrelation factors

We introduce a forecasting technique based on multivariate ideas previously applied in remote sensing. The approach has the trivial but nonetheless fundamental purpose of dividing the information inherent in the time series into important and unimportant. Important information is used for forecasting purposes while the unimportant is discarded. Although related to vector autoregression, giving asymptotically the same estimates, there are reasons to believe that the approach gives better precision of parameter estimates for finite samples as well as more precise predictions.