Efficient pooling of cross-section and time series data using Bayesian machine learning with two econometric applications

在这个项目中，我们采用贝叶斯建模策略，即最小信息长度原则，来解决经济单位（如公司或国家）的有效划分问题，这些单位的行为模式在每个组内相似，但在组间不同。这种方法可以纳入经济理论的要求。由此产生的软件将用于网络开发。我们考虑了两个具体的应用，即模拟经合组织国家的汽油需求，并找到对澳大利亚经济增长率最具预测能力的外国因素。第二项申请涉及相当大的国家利益。

In this project, we adapt a Bayesian modelling strategy, namely the minimum message length principle, to the problem of efficient partitioning of economic units, such as firms or countries, into groups whose behavioural patterns are similar within each group but distinct across groups. This methodology can incorporate the requirements of economic theory. The resulting software will be developed for the Web. We consider two specific applications, namely modelling gasoline demand in OECD countries, and finding the foreign factor with the most predictive power for the growth rate of the Australian economy. The second application is of considerable national interest.