Data Revisions and Real-Time Forecasting
Data Revisions and Real-Time Forecasting
复制标题
数据修正和实时预测
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Galvão
中科院分区：
文献类型：
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作者：
Michael P. Clements;A. Galvão
在给定的时间点，预测者将可以访问经过不同次数修订的宏观经济变量数据，从而导致数据成熟度不同。涉及最近的观测数据将是首次发布的数据，或仅经过几次修订的数据。十年前的意见通常会经过许多轮修订。预测者应该如何使用这些数据来预测未来？传统的方法是使用当时可用的最新数据来估计预测模型，隐含地忽略了观测数据成熟度的差异。
传统的实时预测方法将数据视为给定的，也就是说，它忽略了数据会被修改的事实。在某些情况下，这种方法的成本是点预测和对预测不确定性的评估，其准确性低于明确允许数据修订的预测方法。有几种方法可以“允许数据修订”，包括显式地对数据修订进行建模，不可知或简化形式的方法，以及仅使用大部分未修订的数据。方法的选择部分取决于目标是预测较早的发布还是完全修订的值。
At a given point in time, a forecaster will have access to data on macroeconomic variables that have been subject to different numbers of rounds of revisions, leading to varying degrees of data maturity. Observations referring to the very recent past will be first-release data, or data which has as yet been revised only a few times. Observations referring to a decade ago will typically have been subject to many rounds of revisions. How should the forecaster use the data to generate forecasts of the future? The conventional approach would be to estimate the forecasting model using the latest vintage of data available at that time, implicitly ignoring the differences in data maturity across observations.
The conventional approach for real-time forecasting treats the data as given, that is, it ignores the fact that it will be revised. In some cases, the costs of this approach are point predictions and assessments of forecasting uncertainty that are less accurate than approaches to forecasting that explicitly allow for data revisions. There are several ways to “allow for data revisions,” including modeling the data revisions explicitly, an agnostic or reduced-form approach, and using only largely unrevised data. The choice of method partly depends on whether the aim is to forecast an earlier release or the fully revised values.
使用基于贝叶斯多元年份的 VAR 进行预测
影响因子：
7.9
作者：
Carriero A
通讯作者：
Carriero A