Contemporaneous causality among office property prices of major Chinese cities with vector error correction modeling and directed acyclic graphs

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-12-18 DOI:10.1108/jm2-08-2023-0171

Xiaojie Xu, Yun Zhang

引用次数: 0

Abstract

Purpose This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021. Design/methodology/approach Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices. Findings The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities. Originality/value This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities.

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利用向量误差修正模型和有向无环图分析中国主要城市写字楼价格的同期因果关系

目的本研究旨在探讨 2005-2021 年中国 10 个主要城市办公楼价格指数之间的动态关系。设计/方法/途径作者利用月度数据，采用向量误差修正模型和有向无环图对 10 个指数之间的同期因果关系进行表征。研究结果 PC 算法确定了因果模式，线性非高斯无环模型算法进一步确定了因果路径，我们据此进行了创新核算分析。在价格冲击后的价格调整过程中，我们发现了复杂的价格动态，一线城市通常是价格调整的主导者。原创性/价值这表明，从长远来看，在规划写字楼物业价格政策时，可能需要特别关注一线城市。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.