Modeling and Forecasting Economic Time Series

预测在所有科学中都是至关重要的， 包括经济。 预测的准确性是很重要的 因为预测是用来指导 决策 预报精度的比较 对经济学家来说，更重要的是， 在相互竞争的经济假说之间进行区分。 这个项目 与对经济时间的预测有关， 系列. 它开发了用于构建预测模型的新工具， 构建点预测和区间预测，评估预测 表现，并揭示经验宏观经济学。 这些工具用于实质性的宏观经济应用。 这项研究是全球变化经济学的一部分 这是因为它有助于知识库的建立， 提高今后努力的可信度所需的方法， 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的效果 排放 本研究课题为经济时间序列建模与预测， 八个相互关联的部分：1）马尔可夫转换模型; 2）精确- 自回归模型中的无偏估计和相关预报 模型; 3）低频动态和长记忆模型; 4） 错误指定的预测模型的估计; 5）比较 预测的准确性; 6）评估经济的可预测性 时间序列; 7）用协整模型预测;以及8） 结合预测。

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.