Increase in mean square forecast error when omitting a needed covariate

Mean square errors of ex-post and ex-ante forecasts from transfer function (regression) models are compared with mean square forecast errors of univariate time series models that ignore the covariate. We show that forecasts from the univariate ARMA models are never better, and are usually worse, than the forecasts from the transfer function model.