Combining Many Forecasts with General Loss Functions

将许多预测与一般损失函数相结合

• 批准号: 0111238
• 负责人: Halbert White
• 金额: $ 30.14万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2003-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0111238&HistoricalAwards=false
• 关键词: Combining; Many; Forecasts; General; Loss

Economic time series are often difficult to predict and as a result different forecasters, faced with predicting the same economic variable, often come up with very different answers, reflecting their use of separate forecasting models, information sets and estimation methods. It is very rare for any individual forecast to systematically dominate the others. Many studies have found that a simple equal-weighted combination of forecasts produces better predictions than those generated by individual models. This project develops both theoretical tools and empirical techniques for explaining why simple equal-weighted forecasts do so well in practice and compares these with wider classes of combinations. The research project develops estimation and forecast combination methods that can compete with the equal-weighted forecast combination. We establish conditions under which our proposed methods can be expected to produce improved forecast. The research project also considers the performance of different forecast combination methods under a host of economic circumstances, including prediction of economic variables in the near, medium and distant future, prediction of the entire probability distribution of an economic variable and prediction with loss functions that are tailored to individual policy makers or economic decision makers. The proposal also investigates the possibility of letting the forecast combination weights vary over time since some models may work better in some situations (e.g. when the economy is in a recession) and others may prove to be better in different circumstances (e.g. an expansion state). Forecast combination techniques have already been found to be useful in practical situations. This proposal provides further understanding of why this is so and explores various alternative approaches both theoretically and in practice, particularly when many forecasts are involved, and expands the approach in new ways. The interest in developing these methods is in part driven by practical concerns of the Principal Investigators that has come through interaction with the various Federal Reserve banks and other international organizations such as the IMF and through considering their needs and requests. We expect to continue strong relations with these and other organizations through the grant period.

经济时间序列往往难以预测，因此，不同的预测者在预测同一经济变量时，往往会得出截然不同的答案，这反映出他们使用不同的预测模型、信息集和估计方法。任何一个单独的预测系统地支配其他预测是非常罕见的。许多研究发现，一个简单的等权重组合预测产生更好的预测比那些由个别模型产生。该项目开发了理论工具和经验技术，用于解释为什么简单的等权重预测在实践中表现如此出色，并将其与更广泛的组合进行比较。 该研究项目开发了估计和预测组合方法，可以与等权重预测组合相竞争。我们建立的条件下，我们提出的方法可以预期产生改进的预测。本研究项目还考虑了各种经济情况下不同预测组合方法的性能，包括预测近期、中期和远期的经济变量，预测经济变量的整个概率分布，以及根据政策制定者或经济决策者的个人需求进行损失函数预测。该提案还研究了让预测组合权重随时间变化的可能性，因为某些模型在某些情况下（例如经济衰退时）可能更好，而其他模型在不同情况下（例如扩张状态）可能更好。 人们已经发现预测组合技术在实际情况中很有用。这一建议使人们进一步理解了为什么会这样，并在理论和实践上探讨了各种替代方法，特别是在涉及许多预测的情况下，并以新的方式扩展了这一方法。对开发这些方法的兴趣部分是由主要调查员的实际关切所驱动的，这些关切是通过与各联邦储备银行和其他国际组织（如货币基金组织）的互动以及通过考虑他们的需要和要求而产生的。 我们希望在赠款期间继续与这些组织和其他组织保持密切的关系。