Vector Autoregressions: Forecasting and Reality
Vector Autoregressions: Forecasting and Reality
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向量自回归：预测与现实
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
R. Parker
中科院分区：
文献类型：
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作者：
John C. Rober Tson;R. Parker
构建对实际国内生产总值增长、通货膨胀和失业等经济序列的未来路径的预测，构成了企业和政府应用经济分析的很大一部分。与仅基于专家意见的预测相比，基于模型的预测更容易被独立研究人员复制和验证。此外，预报员可以正式调查模型预测中系统误差的来源，在决策者使用之前可以建立S业绩预测模型。本文作者描述了一种特殊的基于模型的预测方法，即由六个美国宏观经济变量组成的向量自回归。他们将注意力集中在实时应用程序中必须解决的技术障碍和克服这些障碍的方法上，例如处理交错发布数据的条件预测，以及将季度数据与月度数据进行匹配。通过强调使用统计模型预测经济数据的实际问题，作者借鉴了亚特兰大联邦储备银行使用这种模型的经验。虽然所研究的模型很小且高度聚合，但它为说明几个实际预测问题提供了一个方便的框架。对简单模型的关注为潜在用户提供了如何在特定应用程序中实现此类预测模型的路线图。
Constructing forecasts of the future path for economic series such as real gross domestic product growth, inflation, and unemployment forms a large part of applied economic analysis for business and government. Model-based forecasts are easier to replicate and validate by independent researchers than forecasts based on expert opinion alone. In addition, the forecaster can formally investigate the source of systematic errors in model forecasts, and a forecast model s performance can be established before it is used by a decision maker. ; The authors of this article describe a particular model-based forecasting approach, a vector autoregression comprising six U.S. macroeconomic variables. They focus attention on the technical hurdles that must be addressed in a real-time application and methods for overcoming those hurdles, such as conditional forecasting to handle the staggered release of data and matching quarterly with monthly data. ; By emphasizing the practical problems of forecasting economic data using a statistical model, the authors draw on experience in using such a model at the Federal Reserve Bank of Atlanta. Although the model studied is small and highly aggregated, it provides a convenient framework for illustrating several practical forecasting issues. The focus on a simple model provides potential users with a road map of how one might implement such a forecasting model in specific applications.