Large-Model and Adaptive Forecasting in Economics

计算技术和数据可用性的最新进展大大降低了使用大量时间序列数据对经济进行实时预测的成本。与此同时，有相当多的证据表明，经济预测系统有严重的不稳定现象，最近的一个例子是，进入1990年至1991年经济衰退和随后缓慢复苏的大多数模型表现不佳。本研究的目的是开发和分析预测关系随时间变化时的预测方法。几个大数据和丰富的数据集将用于评估各种自适应预测系统的性能。这些数据包括战后美国的数据，二战前美国的数据，以及加拿大和以色列的数据。这项研究很重要，因为它有望显著改善经济预测。一个重要的应用将是改进长期能源预测和评估全球环境变化。

Recent advances in computing technology and data availability have sharply reduced the cost of real-time forecasting of the economy using large numbers of time series data. At the same time, there is considerable evidence that economic forecasting systems have had significant episodes of instability, a recent example being the poor performance of most models entering the 1990 to 1991 recession and the subsequent slow recovery. The purpose of this research is to develop and analyze forecasting methodologies when predictive relationships are changing over time. Several large data and rich data sets will be used to assess the performance of various adaptive forecasting systems. These include data from the postwar U.S., data from the U.S. before World War II, and data from Canada and Israel. This research is important because it holds the promise of improving significantly economic forecasting. An important application would be in the area of improving long-term energy forecasting and assessing global environmental change.