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Economic Forecasting under Macroeconomic Uncertainty

宏观经济不确定性下的经济预测

基本信息

批准号：
ES/K010611/1
负责人：
Ana Galvao
金额：
$ 41.2万
依托单位：
University of Warwick
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FK010611%2F1
关键词：
Economic Forecasting under Macroeconomic Uncertainty

项目摘要

Macroeconomic forecasts are essential inputs in economic decisions such as the decision to invest in a new business project, buy a house, and change the policy interest rates. Macroeconomic forecasts are computed based on models able to capture regularity in past data, such that past economic history help us to anticipate the future values of output growth and inflation. Forecasting models are developed based on different assumptions about the economic structure and the statistical properties of the macroeconomic data. During the 00's, structural models called Dynamic Stochastic General Equilibrium (DSGE), estimated using Bayesian Econometrics techniques, became popular in central banks as a tool to deliver both macroeconomic forecasts and policy analysis. Their popularity was justified by rigorous theoretical background - built on micro-foundations - and by their ability to forecast output and inflation one- and two-years ahead. However, Del Negro and Schorfeide (2012) show the shortcomings of these models in predicting the drop in output observed in the 2008-2009 recession. In contrast, Stock and Watson (2012) argue that a statistical model, called Dynamic Factor Model, was able to capture the severe downturn observed in the United States in 2008-2009. Additional recent forecasting models proposed by the academic literature are: Bayesian Vector Autoregressions and Mixed Data Sampling Models. However, for these state-of-art models, little is known about their capabilities of forecasting under macroeconomic uncertainty. From the point of view of the economic decision maker, it is hard to ascertain if the most adequate set of forecasting models (or a model) is being used, considering the current economic climate. The main output of this research project is a paper evaluating the relative forecasting accuracy of state-of-art macroeconomic forecasting models, including their performance during the recent global downturn.

宏观经济预测是经济决策的基本输入，例如决定投资新商业项目、购买房屋和改变政策利率。宏观经济预测是基于能够捕捉过去数据规律性的模型来计算的，因此，过去的经济历史可以帮助我们预测产出增长和通货膨胀的未来值。预测模型是基于对经济结构和宏观经济数据的统计性质的不同假设而开发的。在20世纪90年代，被称为动态随机一般均衡（DSGE）的结构模型，使用贝叶斯计量经济学技术进行估计，在中央银行中流行起来，作为提供宏观经济预测和政策分析的工具。它们之所以受欢迎，是因为它们有严谨的理论背景（建立在微观基础之上），而且它们有能力预测未来一两年的产出和通胀。然而，Del Negro和Schorfeide（2012）显示了这些模型在预测2008-2009年经济衰退中观察到的产出下降方面的缺陷。相比之下，斯托克和沃森（2012）认为，一种称为动态因素模型的统计模型能够捕捉到2008-2009年在美国观察到的严重衰退。最近学术文献提出的其他预测模型有：贝叶斯向量自回归模型和混合数据抽样模型。然而，对于这些最先进的模型，人们对它们在宏观经济不确定性下的预测能力知之甚少。从经济决策者的角度来看，考虑到当前的经济气候，很难确定是否使用了最适当的一套预测模型（或一个模型）。本研究项目的主要成果是一篇论文，评估了目前最先进的宏观经济预测模型的相对预测精度，包括它们在最近全球经济衰退期间的表现。