联系向量自回归模型

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Applied Stochastic Models in Business and Industry Pub Date : 2024-01-16 DOI:10.1002/asmb.2842
Manabu Asai, Mike K. P. So
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引用次数: 0

摘要

为了适应个体的联系效应,我们为动态面板数据开发了一种新的联系向量自回归(LAR)模型。LAR 模型的一个主要特点是将动态网络信息纳入自回归时间序列建模。动态网络可以是给定的,我们也可以将网络链接表述为历史数据的函数,而函数的未知参数可以从数据中估算出来。我们提出了一种模拟技术来检验 LAR 模型的静态条件,并建议采用贝叶斯马尔科夫链蒙特卡罗方法进行估计。对 45 个国家的国内生产总值(GDP)季度增长率的实证结果表明:(i) 总增长率的自回归(AR)系数很难与零区分开来;(ii) 具有个体效应的面板 AR 模型具有正自相关性;(iii) 与面板 AR 模型相比,替代 LAR 模型更为可取。根据关联变量的规格,关联效应的符号和大小会有所不同。逻辑联系函数具有灵活的结构,可以适应单个 GDP 增长率的联系大小,并能增强动态性。
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Linkage vector autoregressive model

To accommodate linkage effects of individuals, we develop a new linkage vector autoregressive (LAR) model for dynamic panel data. A main feature of the LAR model is incorporating dynamic network information in autoregressive time series modeling. The dynamic network can be given, or we can formulate the network links as a function of historical data, where unknown parameters of the function can be estimated from the data. We propose a simulation technique to check the stationarity condition of the LAR model and suggest a Bayesian Markov chain Monte Carlo method for estimation. Empirical results for the quarterly growth rates of gross domestic product (GDP) in 45 countries indicate that (i) the autoregressive (AR) coefficient for the aggregated growth rate is hard to distinguish from zero; (ii) a panel AR model with individual effects has a positive autocorrelation; and (iii) the alternative LAR models are preferred to the panel AR model. Depending on the specification of the linkage variables, the sign and size of the linkage effect can differ. The logistic linkage function has a flexible structure to accommodate the size of the linkage of individual GDP growth rates, and it strengthens the dynamics.

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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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