VAR Estimation and Forecasting When Data Are Subject to Revision
将Howrey方法推广到数据由复杂统计机构生成的情形，提出一种新的VAR估计与预测方法，在多个经济模型和GDP预测中优于传统VAR和原Howrey方法。
We show that Howrey’s method for producing economic forecasts when data are subject to revision is easily generalized to handle the case where data are produced by a sophisticated statistical agency. The proposed approach assumes that government estimates are efficient with a finite lag. It takes no stand on whether earlier revisions are the result of “news” or of reductions in “noise.” We present asymptotic performance results in the scalar case and illustrate the technique using several simple models of economic activity. In each case, it outperforms both conventional VAR analysis and the original Howrey method. It produces GDP forecasts that are competitive with those of professional forecasters. Special cases and extensions of the analysis are discussed in a series of appendices that are available online.