摘要翻译：
本文引入了两个绝对连续的全局-局部收缩先验子，使得在高维矩阵指数空间规范的背景下随机变量选择成为可能。作为处理空间自回归规范中过参数化问题的一种手段，现有方法通常依赖于计算量要求很高的贝叶斯模型平均技术。所提出的收缩先验值可以用马尔可夫链蒙特卡罗方法灵活有效地实现。对每种收缩先验进行了仿真研究，以评价其性能。结果表明，它们在高维环境中表现得特别好，尤其是当要估计的参数数超过观测数时。为了进行实证说明，我们使用了泛欧区域经济增长数据。
---
英文标题：
《Flexible shrinkage in high-dimensional Bayesian spatial autoregressive
  models》
---
作者：
Michael Pfarrhofer and Philipp Piribauer
---
最新提交年份：
2018
---
分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--

---
英文摘要：
  This article introduces two absolutely continuous global-local shrinkage priors to enable stochastic variable selection in the context of high-dimensional matrix exponential spatial specifications. Existing approaches as a means to dealing with overparameterization problems in spatial autoregressive specifications typically rely on computationally demanding Bayesian model-averaging techniques. The proposed shrinkage priors can be implemented using Markov chain Monte Carlo methods in a flexible and efficient way. A simulation study is conducted to evaluate the performance of each of the shrinkage priors. Results suggest that they perform particularly well in high-dimensional environments, especially when the number of parameters to estimate exceeds the number of observations. For an empirical illustration we use pan-European regional economic growth data.
---
PDF链接：
https://arxiv.org/pdf/1805.10822