Dynamic Factors and Robust Economic Forecasting

9730489股票本项目致力于研究开发预测经济时间序列的新方法，并将这些方法应用于美国宏观经济数据。该研究计划基于四个前提。首先，数据的可用性在过去十年中急剧增加；现在，数以千计的系列可用于实时预测应用。然而，主导经济预测的小型且可以说是高度结构化的大型模型未能利用这一庞大的数据阵列。其次，最近使用正式统计检验的实证研究证实了许多经济时间序列关系随时间不稳定的传统观点。这是许多标准预测方法的一个障碍，但它也提供了一个机会：适当地考虑这种时间变化将改善经济预测。第三，最近的算法和计算进步允许基于非高斯滤波的鲁棒预测方法的发展，这种方法可以比传统的线性或状态空间模型适应更大范围的参数时间变化。第四，经济预测是经济学专业对社会的重要实际贡献，因此改进的经济预测方法本身就构成了一个有效的目的。话虽如此，过去几十年的经验表明，经济预测的工具和结果对应用和理论宏观经济学具有显著的积极溢出效应。这个项目包括两个主要项目。首先是利用现代时间序列分析工具，重新考察扩散指数的构造及其在预测中的应用。在传统的商业周期分析中，扩散指数是衡量经济扩张或衰退在各个部门或地区蔓延的程度。由于这些指数基于许多序列，因此有可能为预测提供有用的信息，而这些信息并不包含在主导现代经济预测学术研究的主要宏观经济总量中。本项目开发的方法是使用动态因子模型定义扩散指标。初步结果表明，只要有足够多的序列，即使存在时变参数，也能准确地估计出这些指标。从183个宏观经济时间序列中提取的因子用于预测四种主要经济总量，初步预测实验取得了可喜的结果。关于稳健预测的第二个主要项目旨在发展和实施预测程序，这些程序对序列之后的过程中的样本外变化和/或样本内错误规范具有稳健性。这项工作借鉴了大量关于鲁棒信号提取和非高斯滤波的文献。有证据表明，与具有传统高斯时变参数不确定性的线性模型相比，这些工具和新的相关工具有可能显著改善宏观经济预测。?？

9730489 Stock This project pursues a research program to develop new methods for forecasting economic time series and to apply these methods to U.S. macroeconomic data. The research program is based on four premises. First, data availability has increased dramatically over the past decade; now literally thousands of series are available for real time forecasting applications. Yet, the small and , arguably, highly structured large models that dominate economic forecasting fail to exploit this vast array of data. Second, recent empirical studies using formal statistical tests confirm conventional wisdom that many economic time series relations are unstable over time. This is an obstacle to many standard forecasting methods, but it also presents an opportunity: properly accounting for this time variation would improve economic forecasts. Third, recent algorithmic and computational advances permit the development of robust forecasting methods based on nonGaussian filtering that can accommodate a wider range of time variation in parameters than conventional linear or state space models. Fourth, economic forecasting is an important practical contribution to the economics profession to society, and as such improved methods for economic forecasting constitute a valid end in themselves. This said, experience over the past several decades has shown that the tools and results of economic forecasting have had significant positive spillovers into applied and theoretical macroeconomics. This project consists oaf two main projects. The first is to use the tools of modern time series analysis to reexamine the construction of diffusion indexes and their use for forecasting. In traditional business cycle analysis, a diffusion index is a measure of the extent to which an expansion or recession has spread across sectors or regions of the economy. Because they are based on many series, these indexes hold out the possibility of providing useful information for forecasting that is not contained in th e main macroeconomic aggregates that dominate modern academic investigations of economic forecasting. They approach developed in this project is to use a dynamic factor model to define diffusion indexes. Preliminary results indicate that, with sufficiently many series, these indexes can be estimated precisely even in the presence of time varying parameters. Promising results are reported here for an initial forecasting experiment, in which factors extracted from 183 macroeconomic time series are used to forecast four main economic aggregates. The second main project on robust forecasting seeks to develop and to implement forecasting procedures that are robust to out-of-sample changes in the process followed by the series and/or to in-sample mispecification. The effort draws on large literatures on robust signal extraction and on nonGaussian filtering. Evidence is presented that these and new, related tools have the potential to result in significant improvements in macroeconomic forecasts relative to linear models with conventional Gaussian time varying parameter uncertainty. ??