Predictive Accuracy Gain From Disaggregate Sampling in ARIMA Models
比较了基于高频和低频数据的ARIMA模型的预测精度，发现从年度数据分解到季度数据能显著降低短期预测误差的方差，但进一步分解到月度数据对月度预测精度提升有限。
Abstract We compare the forecast accuracy of autoregressive integrated moving average (ARIMA) models based on data observed with high and low frequency, respectively. We discuss how, for instance, a quarterly model can be used to predict one quarter ahead even if only annual data are available, and we compare the variance of the prediction error in this case with the variance if quarterly observations were indeed available. Results on the expected information gain are presented for a number of ARIMA models including models that describe the seasonally adjusted gross national product (GNP) series in the Netherlands. Disaggregation from annual to quarterly GNP data has reduced the variance of short-run forecast errors considerably, but further disaggregation from quarterly to monthly data is found to hardly improve the accuracy of monthly forecasts. KEY WORDS: ARMA modelsDisaggregate samplingPrediction