Simultaneous modeling of initial conditions and time heterogeneity in dynamic networks: An application to Foreign Direct Investments
Simultaneous modeling of initial conditions and time heterogeneity in dynamic networks: An application to Foreign Direct Investments
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DOI：
10.1017/nws.2015.3
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发表时间：
2015-03-01
期刊：
影响因子：
1.7
通讯作者：
Lomi, Alessandro
中科院分区：
文献类型：
--
作者：
Koskinen, Johan;Caimo, Alberto;Lomi, Alessandro
在动态网络中，联系的存在既受到内生网络依赖的影响，也受到空间依赖的影响。目前的统计模型通常是相对于一些初始条件来定义的，因此回避了第一个网络来自哪里的问题。此外，虽然这些纵向网络模型可以解释网络随时间变化的动态，但它们不能解释这些动态的变化。我们提出了一个扩展的纵向指数随机图模型，允许同时推断的变化随着时间的推移和初始条件，以及放松时间均匀性的假设。估计借鉴了最近的贝叶斯方法的横截面指数随机图模型和贝叶斯分层模型。这是在1995-2003年全球电力工业外国直接投资关系的背景下发展起来的。众所周知，国际投资关系受到下列因素的影响：（一）由地理位置决定的初始条件;（二）全球投资流动强度随时间的波动;（三）内在的网络依赖性。我们依赖于国际贸易研究中使用的著名引力模型来表示空间嵌入和内生网络依赖如何共同塑造投资关系的动态。
In dynamic networks, the presence of ties are subject both to endogenous network dependencies and spatial dependencies. Current statistical models for change over time are typically defined relative to some initial condition, thus skirting the issue of where the first network came from. Additionally, while these longitudinal network models may explain the dynamics of change in the network over time, they do not explain the change in those dynamics. We propose an extension to the longitudinal exponential random graph model that allows for simultaneous inference of the changes over time and the initial conditions, as well as relaxing assumptions of time-homogeneity. Estimation draws on recent Bayesian approaches for cross-sectional exponential random graph models and Bayesian hierarchical models. This is developed in the context of foreign direct investment relations in the global electricity industry in 1995-2003. International investment relations are known to be affected by factors related to: (i) the initial conditions determined by the geographical locations; (ii) time-dependent fluctuations in the global intensity of investment flows; and (iii) endogenous network dependencies. We rely on the well-known gravity model used in research on international trade to represent how spatial embedding and endogenous network dependencies jointly shape the dynamics of investment relations.