FORECASTING WITH REAL-TIME MACROECONOMIC DATA
FORECASTING WITH REAL-TIME MACROECONOMIC DATA
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DOI：
10.1016/s1574-0706(05)01017-7
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发表时间：
2006-01-01
期刊：
影响因子：
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通讯作者：
Croushore, Dean
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文献类型：
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作者：
Croushore, Dean
预测的好坏取决于预测背后的数据。但是，随着时间的推移和新的源数据的出现以及概念的改变，宏观经济数据往往会被大幅修订。预测如何受到数据修正的影响？为了回答这个问题，我们开始以先行经济指标指数为例来说明实时数据问题。然后，我们研究了为美国数据修订而开发的数据，称为“宏观经济学家实时数据集”，并展示了其基本特征，说明了修订的幅度，从而激发了它们对预测和预测模型的潜在影响。数据集由一组数据年份组成，其中数据年份是指某人观察数据时间序列的日期;因此，1974年9月的数据年份指的是1974年9月某人可用的所有宏观经济时间序列。接下来，我们使用Stark和Croushore（2002），Journal of Macroeconomics 24，507-531，以说明数据修订如何影响合理的单变量预测。在这样做的过程中，我们解决了在评估预测时使用哪些变量作为“实际值”的问题，我们研究了重复观测预测的技术，说明了美国实时数据与最新数据预测的差异，并研究了由各种信息标准决定的模型选择对数据修订的敏感性。第三，我们查看了有关数据修订对预测影响程度的经济文献，包括讨论使用最新数据与最新数据进行预测时的差异，这些影响是否更大或更小取决于预测变量的水平或增长率，数据修订对模型选择和规格说明有多大影响，以及在修订时变量预测内容的证据。鉴于数据会修订，数据修订会影响预测，预测人员应该做些什么？最理想的情况是，预测者在开发预测模型时应该考虑到数据修订。本章的重点是主要与模型开发有关的论文，即试图建立一个更好的预测模型，特别是通过将新模型的预测结果与其他模型的预测结果进行比较，或者与私营部门或政府预测机构在真实的时间内做出的预测结果进行比较。
Forecasts are only as good as the data behind them. But macroeconomic data are revised, often significantly, as time passes and new source data become available and conceptual changes are made. How is forecasting influenced by the fact that data are revised?To answer this question, we begin with the example of the index of leading economic indicators to illustrate the real-time data issues. Then we look at the data that have been developed for U.S. data revisions, called the "Real-Time Data Set for Macroeconomists" and show their basic features, illustrating the magnitude of the revisions and thus motivating their potential influence on forecasts and on forecasting models. The data set consists of a set of data vintages, where a data vintage refers to a date at which someone observes a time series of data; so the data vintage September 1974 refers to all the macroeconomic time series available to someone in September 1974.Next, we examine experiments using that data set by Stark and Croushore (2002), Journal of Macroeconomics 24, 507-531, to illustrate how the data revisions could have affected reasonable univariate forecasts. In doing so, we tackle the issues of what variables are used as "actuals" in evaluating forecasts and we examine the techniques of repeated observation forecasting, illustrate the differences in U.S. data of forecasting with real-time data as opposed to latest-available data, and examine the sensitivity to data revisions of model selection governed by various information criteria.Third, we look at the economic literature on the extent to which data revisions affect forecasts, including discussions of how forecasts differ when using first-available compared with latest-available data, whether these effects are bigger or smaller depending on whether a variable is being forecast in levels or growth rates, how much influence data revisions have on model selection and specification, and evidence on the predictive content of variables when subject to revision.Given that data are subject to revision and that data revisions influence forecasts, what should forecasters do? Optimally, forecasters should account for data revisions in developing their forecasting models. We examine various techniques for doing so, including state-space methods.The focus throughout this chapter is on papers mainly concerned with model development - trying to build a better forecasting model, especially by comparing forecasts from a new model to other models or to forecasts made in real time by private-sector or government forecasters.