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

用于分析宏观经济和金融网络的新横截面相关面板数据方法

基本信息

批准号：
ES/T01573X/1
负责人：
Yongcheol Shin
金额：
$ 42.98万
依托单位：
University of York
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FT01573X%2F1
关键词：
New Cross Sectionally Dependent Panel

项目摘要

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.

在社会科学中，通常使用数据集，其中在多个时间点记录一组实体的信息。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，数据中的残差变化（即模型无法解释的数据变化部分）在实体之间不相关。这被称为横截面独立性。然而，这一假设在实践中经常被违反。控制横截面依赖性（CSD）的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，使得一个实体的行为可能取决于其邻居/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。这是“全局”或“强”CSD。通常，这两个机制可能共同负责可持续发展委员会。然而，在实践中，既考虑空间效应又考虑共同因素的模型很少，而那些确实存在的模型则高度程式化。我们建议开发一个统一的框架来估计复杂而现实的动态异质面板数据模型，该模型考虑了空间依赖性和共同因素。我们将：（i）增加空间动态面板数据模型的灵活性和现实性与共同的因素，通过开发技术，允许模型参数是异质性的个人，不像大多数现有的研究，假设参数同质性。(ii)开发利用空间动态面板数据模型网络结构的方法，为利用这类模型了解全球经济实体之间的双边联系提供新的机会。(iii)将上述讨论的方法从常见的单边（或二维）面板数据扩展到更复杂的双边（三维）面板数据，如贸易和投资流动。我们将应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，研究国家商业周期与所谓的全球商业周期的融合。我们的模型将使我们能够区分由于空间联系（如贸易和政治关系，移民流动等）的影响而产生的趋同。由于全球因素的影响，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。(ii)开发一个新的模式，研究全球贸易流动，并区分空间联系的影响（例如共同边界、自由贸易区成员资格、共同语言等）全球因素（如全球商业周期的状况）。考虑到英国退欧对贸易的影响，这种模型的开发对英国具有战略重要性。(iii)制定一个新的全球股票市场等级模型，其中一个公司的业绩可能取决于空间关系（例如与其部门和/或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、投资者风险规避）。这类模型为经济活动的全球化本质提供了新的见解，并突出了公共和私营部门经济增长的机会和障碍。总之，该项目将做出重大的方法论贡献，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的当代问题。