Bayesian analysis of a dynamic multivariate spatial ordered probit model

IF 1.5 3区经济学 Q2 ECONOMICS

Spatial Economic Analysis Pub Date : 2023-03-10 DOI:10.1080/17421772.2023.2181384

P. Gao, Zixiang Lu

引用次数: 2

Abstract

ABSTRACT Spatial econometrics has few studies on multivariate ordinal responses. This study proposes a dynamic multivariate spatial ordered probit (DMSOP) model, which is the first attempt to capture temporal and spatial dependencies simultaneously for multivariate ordinal responses. The parameters are calculated using Bayesian inference based on Markov chain Monte Carlo sampling. The DMSOP model performs effectively with the simulated data. Furthermore, the DMSOP model is applied to two response variables, namely, the life satisfaction and self-rated health of adults in 25 provinces in China. The empirical results show that the model can effectively measure the spatial and temporal dependencies for multivariate ordinal responses.

查看原文本刊更多论文

动态多元空间有序概率模型的贝叶斯分析

摘要空间计量经济学对多元有序响应的研究很少。本研究提出了一个动态多变量空间有序概率集（DMSOP）模型，这是首次尝试同时捕捉多变量有序响应的时间和空间相关性。使用基于马尔可夫链蒙特卡罗抽样的贝叶斯推断来计算参数。DMSOP模型对模拟数据进行了有效的处理。此外，将DMSOP模型应用于中国25个省份成年人的生活满意度和自评健康两个响应变量。实证结果表明，该模型能够有效地测量多变量有序响应的空间和时间相关性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Spatial Economic Analysis ECONOMICS-

CiteScore

5.40

自引率

21.70%

发文量

期刊介绍： Spatial Economic Analysis is a pioneering economics journal dedicated to the development of theory and methods in spatial economics, published by two of the world"s leading learned societies in the analysis of spatial economics, the Regional Studies Association and the British and Irish Section of the Regional Science Association International. A spatial perspective has become increasingly relevant to our understanding of economic phenomena, both on the global scale and at the scale of cities and regions. The growth in international trade, the opening up of emerging markets, the restructuring of the world economy along regional lines, and overall strategic and political significance of globalization, have re-emphasised the importance of geographical analysis.