Forecasting the Forecasts of Others
Forecasting the Forecasts of Others
复制标题
预测他人的预测
DOI：
--
复制
发表时间：
1983
影响因子：
8.2
通讯作者：
R. Townsend
中科院分区：
文献类型：
--
作者：
R. Townsend
本文探讨了线性投资均衡模型的建立和分析，在该模型中，学习是永恒的，信息分散的企业永远不需要共享与其决策相关的时间序列的相同信念。递归卡尔曼滤波技术被证明适用于说明性的分层信息结构，而待定系数的非线性技术被证明适用于其中存在运动规律与预测问题的混淆的说明性对称信息结构。这些模型的平衡时间序列可以在冲击和测量误差的响应下显示出有趣的运动，包括持久性、某些互相关性和衰减振荡。也就是说，预测误差在决策者中是连续相关的，并且在某种关键意义上是随着时间的推移而连续相关的。更广泛地说，这些模型确实对观察到的时间序列施加了限制，并可以与数据进行拟合。
This paper explores the formulation and analysis of linear equilibrium models of investment in which learning is perpetual and informationally decentralized firms need never share the same beliefs concerning time series relevant to their decisions. Recursive, Kalman filtering techniques are shown to be applicable in an illustrative, hierarchical information structure, and a nonlinear technique of undetermined coefficients is shown to be applicable in an illustrative, symmetric information structure in which there is a confounding of laws of motion with forecasting problems. The equilibrium time series of these models can display interesting movement in response to shocks and measurement errors, including persistence, certain cross-correlation properties, and damped oscillations. That is, forecasts errors are serially correlated over decision makers and serially correlated over time in a certain crucial sense. More generally, these models do place restrictions on observed time series and can be fitted to data.