A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series
比较了多种自动单变量自回归预测方法在150个宏观经济时间序列上的实际表现，发现Akaike滞后长度选择准则在多种情况下表现良好，长记忆成分建模对多期预测重要，而预测的线性组合并未显著提升质量。
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. KEY WORDS: Akaike criterionAutoregressionARIMAARARMAForecastingMacroeconomic time series