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

使用贝叶斯机器学习和两个计量经济学应用程序，有效地汇集横截面和时间序列数据。在这个项目中，我们采用贝叶斯建模策略，即最小消息长度原则，来解决将经济单位(如公司或国家)有效划分为行为模式在每个组内相似但在组内不同的组的问题。这种方法论可以结合经济理论的要求。由此产生的软件将为网络开发。我们考虑了两个具体的应用，即对OECD国家的汽油需求进行建模，以及寻找对澳大利亚经济增长率最具预测力的外国因素。第二项申请涉及相当大的国家利益。

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