GLASS - The Global Augmented State Space Error Correction Model: Structure Theory, Estimation and Inference

GLASS - 全局增强状态空间纠错模型：结构理论、估计和推理

• 批准号: 469278259
• 负责人: Professor Dr. Dietmar Bauer
• 金额: --
• 依托单位: Lehrstuhl für Ökonometrie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/469278259?language=en
• 关键词: GLASS; Global; Augmented; State; Space

Increased economic and financial integration, increased availability of large-scale (multi-country) data sets and scientific progress in econometric modelling of high-dimensional time series have led to important advances in multi-country modelling. These advances have occurred both in economic theory driven models and reduced form time series models specified using statistical tools rather than economic theory. For empirical application any model class under consideration has to deal with the curse-of-dimensionality or, put differently, complexity reduction. In this project we aim to (i) explore and (ii) extend the linkages between two prominent econometric approaches for modelling high-dimensional (structured) time series, global vector autoregressive (GVAR) and generalized dynamic factor models (GDFMs). GVAR models achieve complexity reduction by strongly restricting the impact of the variables of all other countries on the evolution of the variables in each country considered. The impact of other countries on the evolution is purged in the so-called star and global variables. The corresponding restrictions on the one hand rest upon numerous posited exogeneity constraints and also limit the flexibility of modelling the long-run (cointegration) behavior of the joint system. Whilst in particular “structural forms” of GVAR models can be seen as semi-structural models, GDFMs have a strong reduced form character, relating in particular the co-movements of series to a small number of unobserved and statistically identified common factors. This approach is very efficient in complexity reduction, but limits in particular structural analysis and interpretations.Starting from the underlying assumption that all variables are generated by a vector autoregressive moving average (VARMA) process this project proposes – using appropriate state space representations – the Global Augmented State Space (GLASS) model to (i) address open issues with respect to exogeneity and cointegration properties in GVAR-type models in a model-class that is invariant to linear transformations and (ii) to investigate in detail the relationship between (VARMA) GDFMs and GLASS-type models. The structure of state space models with the latent (potentially low-dimensional) state vector describing the dynamics of the observables is very similar to the structure of GDFMs; providing the entry point for studying the relationships. A key issue to be addressed to study the links across model classes is a detailed understanding of both finite-N (in GVAR settings) as well as N-asymptotic (GDFMs) properties and their interrelationships. Based upon the structure theory, GLASS will develop estimation and inference tools allowing for structural analysis in high-dimensional cointegrated systems. The procedures and tools will be made available to the public by means of well-tested and robust code. GLASS is a high-dimensional and structural extension of our earlier project EICIP.

经济和金融一体化程度提高、大规模(多国)数据集的可获得性增加以及高维时间序列计量经济学建模方面的科学进步导致了多国建模方面的重大进展。这些进展既发生在经济理论驱动的模型中，也发生在使用统计工具而不是经济理论指定的简化形式的时间序列模型中。对于经验应用，考虑中的任何模型类都必须处理维度诅咒，或者换一种说法，复杂性降低。在这个项目中，我们的目标是(I)探索和(Ii)扩展两种用于高维(结构化)时间序列建模的重要计量经济学方法之间的联系，即全局向量自回归(GVAR)和广义动态因素模型(GDFM)。GVAR模型通过严格限制所有其他国家的变量对所考虑的每个国家的变量演变的影响来实现复杂性降低。其他国家对进化的影响在所谓的恒星和全球变量中被清除。相应的限制一方面依赖于许多假定的外生约束，也限制了对联合系统的长期(协整)行为进行建模的灵活性。虽然GVAR模型的特定“结构形式”可以被视为半结构模型，但GDFM具有很强的简化形式特征，特别是将序列的共同运动与少数未观察到和统计上确定的共同因素联系在一起。从所有变量都是由向量自回归滑动平均(VARMA)过程产生的基本假设出发，利用适当的状态空间表示，本项目提出了全局增广状态空间(GERS)模型，以(I)在不随线性变换变化的模型类中解决GVAR类型模型的外生性和协整性质方面的未决问题，以及(Ii)详细研究(VARMA)GDFM和玻璃型模型之间的关系。具有潜在(潜在低维)状态向量的状态空间模型的结构非常类似于GDFM的结构，为研究这些关系提供了切入点。要研究模型类之间的联系，需要解决的一个关键问题是详细了解有限N(在GVAR设置下)以及N-渐近(GDFM)性质及其相互关系。在结构理论的基础上，玻璃将开发评估和推理工具，允许在高维协整系统中进行结构分析。这些程序和工具将通过经过良好测试和健壮的代码向公众提供。玻璃是我们早期项目EICIP的高维和结构延伸。