Abstract
This paper investigates a biased regression approach to the preliminary estimation of the Box-Jenkins transfer function weights. Using statistical simulation to generate time series, 14 estimators (various OLS, ridge and principal components estimators) are compared in terms of MSE and standard error of the weight estimators. The estimators are investigated for different levels of multicollinearity, signal-to-noise ratio, number of independent variables, length of time series and number of lags included in the estimation. The results show that the ridge estimators nearly always give lower MSE than the OLSestimator, and in the computationally difficult cases give much lower MSE than the OLS estimator. The principal components estimators can give lower MSE than the OLS, but also higher values. All biased estimators nearly always give much lower estimated standard error than OLS when estimating the weights.