Development of a Bayesian estimator for non-stationary Markov transition probabilities and its application to EU farm structural change

非平稳马尔可夫转移概率的贝叶斯估计量的开发及其在欧盟农场结构变化中的应用

• 批准号: 193147094
• 负责人: Professor Dr. Thomas Heckelei
• 金额: --
• 依托单位: Professur für Wirtschafts- und Agrarpolitik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2013-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/193147094?language=en
• 关键词: Development; Bayesian; estimator; non; stationary

The agricultural sector has experienced substantial structural changes in the past and faces continuing adjustments in the future. The implications of structural change are not only relevant for the sector itself but have broader social, economic and environmental consequences for a region. An understanding of this process is required in order to assess how (agricultural-) policy affects or, if a specific social outcome is desired, can influence this development. A common approach to gain understanding of the process is to model structural change as a Markov process. One problem in the analysis of structural change in the EU is that farm level (micro) data is rarely available such that inference about behaviour of individual farms has to be derived from aggregated (macro) data. Recently, the generalized cross entropy estimator gained popularity in this context since it allows considering prior information such that the often underdetermined “macro data” Markov models can be estimated. However, the way prior information is considered is also the greatest drawback of the approach. Therefore, the project aims to develop a Bayesian framework as an alternative estimator that allows to consider prior information in a more efficient and transparent way. The project will further provide an evaluation of the statistical properties of the estimator as well as an exemplifying application analyzing the effects of single farm payments on agricultural structural change in the EU.

农业部门过去经历了重大的结构性变化，未来将面临持续的调整。结构变化的影响不仅与该部门本身有关，而且对一个区域具有更广泛的社会、经济和环境后果。必须了解这一进程，以便评估（农业）政策如何影响这一发展，或者如果希望取得特定的社会结果，如何影响这一发展。理解这一过程的一种常用方法是将结构变化建模为马尔可夫过程。欧盟结构变化分析中的一个问题是，农场层面（微观）数据很少可用，因此对单个农场行为的推断必须从总体（宏观）数据中得出。最近，广义交叉熵估计器在这方面得到了普及，因为它允许考虑先验信息，从而可以估计经常不确定的“宏观数据”马尔可夫模型。然而，考虑先验信息的方式也是该方法最大的缺点。因此，该项目旨在开发一个贝叶斯框架作为替代估计器，允许以更有效和透明的方式考虑先验信息。该项目将进一步对估算器的统计特性进行评估，并举例分析单一农场支付对欧盟农业结构变化的影响。