The Analysis of Non-stationary Time Series in Economics and Finance: Co-integration, Trend Breaks, and Mixed Frequency Data

经济和金融中的非平稳时间序列分析：协整、趋势突破和混合频率数据

• 批准号: ES/M01147X/1
• 负责人: Anthony Taylor
• 金额: $ 35.75万
• 依托单位: University of Essex
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FM01147X%2F1
• 关键词: Analysis; Non; stationary; Time; Series

Macroeconomic and financial time series are typically non-stationary (or unstable), in that their means, variances and autocovariances evolve over time, such that standard multivariate time series models can only be validly applied to the changes in these variables. Such models, however, contain no information about any long run relationships between the series, as are often predicted by economic or finance theory. A solution is provided by co-integration analysis which recognises that certain combinations of the variables are stationary (stable). A key example is term structure data, where it is often found that while individual interest rates appear to be unstable, the spreads between the rates appear stable.Practical co-integration analysis is complicated by the fact that economies periodically undergo episodes of structural change, such as stock market crashes or changes in government regime/policy. Empirical evidence suggests that these episodes often manifest themselves in the form of multiple changes in the underlying deterministic trend component of the variables and/or changes in the volatility of the unanticipated random shocks. Extant co-integration tests can result in misleading inference regarding the presence or otherwise of long run relationships between the variables when these forms of structural change are present. This will typically result in misspecified econometric models with poor forecasting ability. It is therefore important to develop new co-integration tests which can deliver reliable inference in such environments. Doing so constitutes the first part of this project and will involve the development of a new simulation-based (bootstrap) procedure.In light of the recent financial crisis, attention has increasingly focused on understanding the interactions between the macroeconomy and the financial sector. To do so effectively, econometric methods are needed that are capable of handling the mismatch between the frequencies at which data on the financial sector (eg exchange rates, stock prices) and the macroeconomy (eg GDP) become available, and this constitutes the second part of the project. While financial data can be observed at very high frequencies, macroeconomic data are typically available only monthly at best. The vast majority of methods for modelling multivariate time series assume a common sampling frequency; this typically entails discarding information in the high frequency data by converting it to the lowest frequency. However, high frequency financial data contains information that can affect the future time path of the low frequency data, and its utilisation can enable policymakers to act promptly prior to the macroeconomic data becoming available. For example, a financial crisis can be observed long before its effects on GDP are observed, but the ability to predict what those effects might be, using an econometric model capable of dealing with mixed frequency data, can be an important aid to policy making. Methods to allow for structural changes when dealing with mixed frequency data will also be considered.The theoretical development, to be conducted using large sample econometric theory, will exploit the expertise and experience of the applicants. Taylor has already examined the behaviour of non-constant volatility on co-integration tests which do not allow for structural change in the trend. Chambers has recently developed methods of combining mixed frequency data that preserve the underlying relationships between the series and has also analysed co-integrated systems under temporal aggregation. This project will build on these foundations.The practical relevance of the theoretical results will be explored using simulation experiments. We will also provide clear guidance to empirical researchers, through worked examples on key international datasets, and make freely available computer programs, to facilitate the implementation of the new techniques.

宏观经济和金融时间序列通常是非平稳（或不稳定）的，因为它们的均值、方差和自协方差随着时间的推移而变化，因此标准多元时间序列模型只能有效地应用于这些变量的变化。然而，这些模型不包含有关序列之间任何长期关系的信息，正如经济或金融理论通常预测的那样。协整分析提供了一个解决方案，该分析认识到变量的某些组合是固定的（稳定的）。一个关键的例子是期限结构数据，人们经常发现，虽然个人利率似乎不稳定，但利率之间的利差却似乎稳定。经济周期性地经历结构性变化（例如股市崩盘或政府政权/政策变化）的事实使实际协整分析变得复杂。经验证据表明，这些事件通常以变量的潜在确定性趋势成分的多重变化和/或意外随机冲击的波动性变化的形式表现出来。当存在这些形式的结构变化时，现有的协整检验可能会导致关于变量之间是否存在长期关系的误导性推论。这通常会导致计量经济模型指定错误，预测能力较差。因此，开发新的协整测试非常重要，它可以在此类环境中提供可靠的推理。这样做构成了该项目的第一部分，并将涉及开发新的基于模拟（引导）程序。鉴于最近的金融危机，人们的注意力越来越集中在理解宏观经济与金融部门之间的相互作用上。为了有效地做到这一点，需要能够处理金融部门数据（例如汇率、股票价格）和宏观经济数据（例如GDP）可用频率之间不匹配的计量经济学方法，这构成了该项目的第二部分。虽然金融数据可以非常频繁地观察到，但宏观经济数据通常最多只能每月获得一次。绝大多数多元时间序列建模方法都假设有一个共同的采样频率；这通常需要通过将高频数据转换为最低频率来丢弃高频数据中的信息。然而，高频金融数据包含可能影响低频数据未来时间路径的信息，其利用可以使政策制定者在宏观经济数据可用之前迅速采取行动。例如，金融危机可以在其对 GDP 的影响被观察到之前就被观察到，但是使用能够处理混合频率数据的计量经济模型来预测这些影响可能是什么的能力可以为政策制定提供重要帮助。还将考虑在处理混合频率数据时允许结构变化的方法。使用大样本计量经济学理论进行的理论开发将利用申请人的专业知识和经验。泰勒已经在协整检验中研究了非恒定波动性的行为，该检验不允许趋势发生结构性变化。钱伯斯最近开发了组合混合频率数据的方法，保留了序列之间的潜在关系，并且还分析了时间聚合下的协整系统。该项目将建立在这些基础上。将通过模拟实验探索理论结果的实际相关性。我们还将通过关键国际数据集的工作示例为实证研究人员提供明确的指导，并免费提供计算机程序，以促进新技术的实施。

项目成果

The Estimation of Continuous Time Models with Mixed Frequency Data

混合频率数据连续时间模型的估计

• 发表时间: 2016
• 作者: Chambers, M J

Deterministic Parameter Change Models in Continuous and Discrete Time

连续和离散时间的确定性参数变化模型

• DOI: 10.1111/jtsa.12456
• 发表时间: 2019
• 期刊: Journal of Time Series Analysis
• 影响因子: 0.9
• 作者: Chambers M

Continuous Time Modelling Based on an Exact Discrete Time Representation. Working Paper

基于精确离散时间表示的连续时间建模。

• 发表时间: 2017
• 作者: Chambers MJ

Frequency Domain Estimation of Continuous Time Cointegrated Models with Mixed Frequency and Mixed Sample Data

混合频率和混合样本数据的连续时间协整模型的频域估计

• DOI: 10.1111/jtsa.12461
• 发表时间: 2019
• 期刊: Journal of Time Series Analysis
• 影响因子: 0.9
• 作者: Chambers M

DETERMINING THE COINTEGRATION RANK IN HETEROSKEDASTIC VAR MODELS OF UNKNOWN ORDER

确定未知阶异方差 VAR 模型中的协整秩

• DOI: 10.1017/s0266466616000335
• 发表时间: 2016
• 期刊: Econometric Theory
• 影响因子: 0.8
• 作者: Cavaliere G