A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series

Abstract

The actual performance of several automated univariate autoregressive forecasting procedures, applied to 150 macroeconomic time series, are compared. The procedures are the random walk model as a basis for comparison; long autoregressions, with three alternative rules for lag length selection; and a long autoregression estimated by minimizing the sum of absolute deviations. The sensitivity of each procedure to preliminary transformations, data, periodicity, forecast horizon, loss function employed in parameter estimation, and seasonal adjustment procedures is examined. The more important conclusions are that Akaike's lag-length selection criterion works well in a wide variety of situations, the modeling of long memory components becomes important for forecast horizons of three or more periods, and linear combinations of forecasts do not improve forecast quality appreciably.