Dynamic Factors and Robust Economic Forecasting

9730489库存 该项目致力于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;现在有数千个系列可用于真实的时间预测应用。 然而，主导经济预测的小型、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。 其次，最近使用正式统计测试的实证研究证实了传统观点，即许多经济时间序列关系随着时间的推移是不稳定的。 这对许多标准预测方法来说是一个障碍，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，最近的算法和计算的进步，允许发展强大的预测方法的基础上非高斯滤波，可以容纳更广泛的时间变化的参数比传统的线性或状态空间模型。 第四，经济预测是经济学专业对社会的一个重要的实际贡献，因此，改进经济预测方法本身就是一个有效的目的。 尽管如此，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产生了重大的积极溢出效应。 这个项目包括两个主要项目。 第一种是利用现代时间序列分析工具重新审视扩散指数的构建及其预测用途。 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经济部门或地区之间蔓延的程度。 因为它们是基于许多系列，这些指数提供了有用的信息，预测是不包含在主要的宏观经济总量，主导现代学术研究的经济预测的可能性。 本计画所发展的方法是利用动态因子模式来定义扩散指数。 初步结果表明，有足够多的系列，这些指标可以精确估计，即使在存在时变参数。 这里报告的初步预测实验，其中提取的因素从183个宏观经济时间序列被用来预测四个主要的经济总量的可喜成果。 关于稳健预测的第二个主要项目旨在开发和实施预测程序，这些程序对序列后续过程中的样本外变化和/或样本内错误指定具有稳健性。 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。 证据表明，这些新的，相关的工具有可能导致显着改善宏观经济预测相对于线性模型与传统的高斯时变参数的不确定性。 ??

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. ??