Modeling and Forecasting Economic Time Series

经济时间序列建模与预测

• 批准号: 9210846
• 负责人: Francis Diebold
• 金额: --
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-15 至 1994-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9210846&HistoricalAwards=false
• 关键词: Modeling; Forecasting; Economic; Time; Series

Prediction is of fundamental importance in all the sciences, including economics. Forecast accuracy is of obvious importance to users of forecasts because forecasts are used to guide decisions. Comparisons of forecast accuracy are also of importance to economists more generally, who are interested in discriminating among competing economic hypotheses. This project is unified by concern with the forecasting of economic time series. It develops new tools for building forecasting models, constructing point and interval forecasts, evaluating forecast performance, and uncovering empirical macroeconomic regularities. These tools are used in substantive macroeconomic applications. This research is part of the economics of global change initiative because it contributes to the knowledge base of methods needed to enhance the credibility of future efforts to predict the economic impacts of global environmental change or the effects of policies designed to reduce greenhouse gas emissions. This project on modeling and forecasting economic time series has eight interrelated parts: 1) markov-switching models; 2) exactly- unbiased estimators and associated predictors in autoregressive models; 3) low-frequency dynamics and long-memory models; 4) estimation of misspecified forecasting models; 5) comparing predictive accuracy; 6) assessing the predictability of economic time series; 7) forecasting with cointegrated models; and 8) combining forecasts.

预测对于所有科学（包括经济学）都至关重要。 预测准确性对于预测用户来说显然很重要，因为预测用于指导决策。 预测准确性的比较对于更广泛的经济学家来说也很重要，因为他们有兴趣区分相互竞争的经济假设。 该项目统一关注经济时间序列的预测。 它开发了用于构建预测模型、构建点和区间预测、评估预测绩效以及揭示经验宏观经济规律的新工具。 这些工具用于实质性宏观经济应用。 这项研究是全球变化经济学倡议的一部分，因为它有助于建立所需方法的知识库，以提高未来预测全球环境变化的经济影响或旨在减少温室气体排放的政策效果的努力的可信度。 这个经济时间序列建模和预测项目有八个相互关联的部分：1）马尔可夫切换模型； 2）自回归模型中的精确无偏估计量和相关预测量； 3）低频动态和长记忆模型； 4）对错误指定的预测模型的估计； 5）比较预测准确性； 6）评估经济时间序列的可预测性； 7）利用协整模型进行预测； 8) 综合预测。