New Cross-Sectionally Dependent Panel Data Methods for the Analysis of Macroeconomic and Financial Networks

在社会科学中，通常使用在多个时间点记录一组实体的信息的数据集。这就是所谓的面板数据，它构成了纵向分析的基础。与所有统计模型一样，面板数据模型依赖于假设。面板数据模型的一个常见假设是，数据中的剩余变化（即模型无法解释的数据变化的那部分）在实体之间是不相关的。这就是所谓的横截面独立性。然而，这个假设在实践中经常被违背。横截面依赖性（CSD）控制方法的发展是一个活跃的研究领域。CSD可以通过两种机制产生。首先，数据可能表现出空间依赖性，例如一个实体的行为可能依赖于其邻居/对等体的行为。这通常被称为“局部”或“弱”CSD。第二，所有实体的数据可能受到一个或多个共同因素的影响。这就是“全球的”或“强大的”CSD。通常，这两种机制可能共同负责可持续发展。然而，在实践中，同时考虑空间效应和共同因素的模型是罕见的，而那些确实存在的模型是高度程式化的。我们建议开发一个统一的框架，用于估计复杂和现实的动态异构面板数据模型，该模型考虑了空间依赖性和共同因素。这个项目将产生三个重要的方法论进步。我们将：(i)通过开发允许模型参数在个体之间异构的技术，增加具有共同因素的空间动态面板数据模型的灵活性和现实性，这与大多数假设参数同质性的现有研究不同。（ii）开发利用空间动态面板数据模型的网络结构的方法，为使用这类模型来理解全球经济实体之间的双边联系开辟新的机会。（iii）将上面讨论的方法从单边（或二维）面板数据的常见情况扩展到更复杂的双边（三维）面板数据情况，如贸易和投资流动。我们将运用我们开发的方法来研究全球化的三个重要方面。我们将：(i)开发一个新的模型来研究国家经济周期与所谓的全球经济周期的趋同。我们的模型将使我们能够将由于空间联系（如贸易和政治关系、移民流动等）的影响而产生的趋同与由于全球因素影响而产生的趋同区分开来。这一模型将有助于在一个相互关联的世界中指导经济稳定政策的设计。㈡开发一种新模式，研究全球贸易流动，并将空间联系（如共同边界、自由贸易区成员、共同语言等）的影响与全球因素（如全球商业周期状况）区分开来。考虑到英国脱欧对贸易的影响，这些模式的发展对英国具有战略重要性。（iii）建立一个新的全球股票市场等级模型，其中公司的业绩可能取决于空间关系（例如与该部门和/或其地理区域内其他公司的联系）以及一系列共同因素（例如流动性，投资者风险厌恶）。这种类型的模型为经济活动的全球化性质提供了新的见解，并突出了公共和私营部门经济增长的机会和障碍。总而言之，该项目将在方法论上做出重大贡献，并将利用这些贡献来解决政策制定者和专业经济学家面临的紧迫当代问题。

In the social sciences, it is common to use datasets in which information for a group of entities is recorded at multiple points in time. This is known as panel data and it forms the basis for longitudinal analysis. As with all statistical models, panel data models rely on assumptions. One common assumption of panel data models is that the residual variation in the data (i.e. that part of the variation in the data that the model cannot explain) is uncorrelated across entities. This is known as cross-sectional independence. However, this assumption is frequently violated in practice. The development of methods to control for cross-sectional dependence (CSD) is an active area of research.CSD can arise through two mechanisms. First, the data may exhibit spatial dependence, such that the behaviour of one entity may depend on the behaviour of its neighbours/peers. This is often called 'local' or 'weak' CSD. Second, the data for all entities may be influenced by one or more common factors. This is 'global' or 'strong' CSD. Often, both mechanisms may be jointly responsible for CSD. However, in practice, models that account for both spatial effects and common factors are rare, and those that do exist are highly stylised. We propose to develop a unifying framework for the estimation of sophisticated and realistic dynamic heterogeneous panel data models that account for spatial dependence and common factors.This project will generate three significant methodological advances. We will:(i) increase the flexibility and realism of spatial dynamic panel data models with common factors by developing techniques that allow for the model parameters to be heterogeneous across individuals, unlike most existing studies that assume parameter homogeneity.(ii) develop methods to exploit the network structure of spatial dynamic panel data models, opening new opportunities to use models of this type to understand the bilateral linkages among entities in the global economy.(iii) extend the methods discussed above from the common case of unilateral (or 2-dimensional) panel data to the more complex case of bilateral (3D) panel data, such as trade and investment flows.We will apply the methodologies that we develop to study three important aspects of globalisation. We will:(i) develop a new model to study the convergence of national business cycles onto a so-called global business cycle. Our model will allow us to separate convergence due to the effect of spatial linkages (e.g. trade and political relations, migration flows etc.) from convergence due to the influence of global factors. This model will help to guide the design of economic stabilisation policy in an interconnected world.(ii) develop a new model to study global trade flows and to separate the influence of spatial linkages (e.g. common borders, membership of free trade areas, common languages etc.) from global factors (e.g. the state of the global business cycle). The development of such models is of strategic importance to the UK, given the trade implications of Brexit.(iii) develop a new hierarchical model of global stock markets, where the performance of a firm may depend on spatial relations (e.g. linkages to other firms in its sector and/or in its geographical region) as well as a range of common factors (e.g. liquidity, investor risk aversion). Models of this type provide new insights into the globalised nature of economic activity and highlight opportunities and obstacles to economic growth for both the public and private sector.In sum, this project will make significant methodological contributions and will leverage these contributions to address pressing contemporary issues facing policymakers and professional economists alike.