Forecasting Using Non-Linear and Generalized Error-Correction Models

使用非线性和广义误差校正模型进行预测

• 批准号: 9600674
• 负责人: Clive Granger
• 金额: $ 6.22万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-01 至 1997-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9600674&HistoricalAwards=false
• 关键词: Forecasting; Using; Non; Linear; Generalized

Clive W. Granger University of California - San Diego SBR-9600674 Forecasting is a widely used tool in policy analysis. The P.I. will apply recent developments in nonlinear time-series models to improve forecasting models. Among the areas to be investigated include error-correction models and duration models. Error- correction models will be generalized to include nonlinear forms. Duration models, which relate to the time period "news" has effect on a market, will be investigated to determine their relevance for forecasting.

预测是政策分析中广泛使用的工具。pi将应用非线性时间序列模型的最新发展来改进预测模型。要研究的领域包括误差校正模型和持续时间模型。误差修正模型将被推广到包括非线性形式。持续时间模型与“新闻”对市场产生影响的时间周期有关，将进行调查，以确定其与预测的相关性。