Bayesian Analysis of Haavelmo's Models
Haavelmo 模型的贝叶斯分析AI 解读 ↓
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Haavelmo 模型的贝叶斯分析
- 研究问题
- Haavelmo 模型的参数推断如何受误差自相关、投资外生性及先验选择影响？摘要还研究给定外生投资水平时的预测分布。
- 方法与思路
- 作者对模型 I 使用局部均匀先验，推导精确后验与预测密度，并通过一阶自回归误差、投资内生的模型 II 及替代先验开展敏感性分析。
- 材料陈述的贡献
- 摘要称获得精确有限样本结果并以 Haavelmo 数据计算后验；其中投资外生性对该数据的参数推断十分关键，另展示先验如何被样本更新。
- 阅读关注点
- 应核对先验的具体定义与后验适定性、误差自相关及投资内生的模型设定，并检查条件分布比较如何支撑外生性敏感的判断。
- 阅读边界
- 摘要未提供数据细节、后验数值或假设检验标准；精确有限样本计算不等于模型设定正确，外生性的重要性结论也限于文中数据。
原文摘要
In this paper, the exact posterior distributions of the parameters of Haavelmo's model I is derived for locally uniform prior distributions. Marginal distributions of the parameters have been obtained for Haavelmo's data. Then the predictive probability density of the model is derived for given values of the exogenous variable, investment. In order to check some of the specifying assumptions, the model is expanded and analyzed under the assumption that the error terms are generated by a first order autoregressive scheme. Exact finite sample results are obtained and the posterior distributions are computed for Haavelmo's data. Conditional distributions of the parameters of the model are computed for given values of the autocorrelation parameter, p, in order to assess the effects of departures from our specifying assumptions. Another specifying assumption that is examined concerns the exogenous nature of investment. For this. Haavelmo's model II, in which investment is assumed to be endogenous, is used. Posterior distributions of the parameters of the model are computed for this model. The sensitiveness of the inference about the parameters of the model to the assumption that investment is exogeneous is studied by computing various conditional distributions for model II. It is seen that this assumption is very crucial for Haavelmo's data. Finally, tvAo different pr-ior distribLutions reflecting two different views about investmeint are introduced. The posterior distributions of the same parameter are then used to determille how one's prior belief is modified by the sample information.