Economic Forecasting under Macroeconomic Uncertainty

宏观经济预测是经济决策的重要输入，例如投资新业务项目，购买房屋和改变政策利率的决定。宏观经济预测是基于能够捕捉过去数据规律的模型计算的，因此过去的经济历史有助于我们预测产出增长和通货膨胀的未来值。预测模型是根据对经济结构和宏观经济数据的统计特性的不同假设开发的。在20世纪00年代，使用贝叶斯计量经济学技术估计的称为动态随机一般均衡（DSGE）的结构模型在中央银行中流行，作为提供宏观经济预测和政策分析的工具。它们的受欢迎程度是由严格的理论背景（建立在微观基础上）和它们预测未来一年和两年产出和通胀的能力所证明的。然而，Del Negro和Schorfeide（2012）指出了这些模型在预测2008-2009年经济衰退中观察到的产出下降方面的缺陷。与此相反，Stock and沃森（2012）认为，一个名为动态因子模型的统计模型能够捕捉到2008-2009年美国出现的严重衰退。最近学术文献提出的其他预测模型有：贝叶斯向量自回归和混合数据抽样模型。然而，对于这些最先进的模型，很少有人知道他们的预测能力下的宏观经济不确定性。从经济决策者的角度来看，考虑到当前的经济环境，很难确定是否使用了最适当的一套预测模型（或模型）。该研究项目的主要成果是一份评估最先进的宏观经济预测模型的相对预测准确性的文件，包括它们在最近全球经济衰退期间的表现。

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.