Crisis Econometrics and High Dimensional Nonstationary Regression

危机计量经济学和高维非平稳回归

• 批准号: 1258258
• 负责人: Peter Phillips
• 金额: $ 29.47万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1258258&HistoricalAwards=false
• 关键词: Crisis; Econometrics; Dimensional; Nonstationary; Regression

Financial history shows that asset price bubbles, crises and panics are intermittent but perennial characteristics of world financial markets. Experience from the Global Financial Crisis (GFC) over 2007-2009 and the present sovereign debt crisis underscores these historical lessons and bears witness to the cost of financial instability in terms of real economic activity and employment. New regulatory initiatives on capital requirements and the Basel III international financial accord have emphasized the need for improved surveillance of banks and financial markets to help avoid the "excessive credit creation" that typically accompanies asset price bubbles. An important practical issue in such market surveillance involves the assessment of what is "excessive" in the available evidence. Central bank economists and regulators cannot work to offset a speculative bubble unless they are able to assess whether one exists.A prominent recent example of this conundrum occurred during the early phase of the 1990s Internet Bubble which created and destroyed $8 trillion in shareholder wealth in less than a decade. In 1996 Alan Greenspan famously spoke of "irrational exuberance", expressing concern about possible asset price inflation but with no supporting econometric evidence and no effect on market escalation or the subsequent crash. Similarly in the early 2000s, repeated warnings of excessive rises in house prices by the economist Robert Shiller failed to alert policy makers of the emerging speculative bubble in housing.Econometric work can assist this complex exercise of monitoring financial markets by quantifying market excesses in relation to fundamentals. Research by the Principal Investigator (PI) has provided an early warning alert system of financial exuberance and market stress. Some of the PI's methods have been adopted by central bank research and surveillance teams to enhance the monitoring of financial asset, real estate, and commodity price markets. This work has involved econometric analysis that focuses on the revealed properties of individual financial time series and the presence of exuberance in the data. The current NSF project extends that work on detection techniques to provide anticipative dating algorithms that can help regulators in market-monitoring activity where there is risk of financial contagion and crisis concatenation over time and across different markets. Evidence-based warning diagnostics are useful as alert mechanisms for market participants as well as for regulators. But to be effective in financial surveillance and regulatory work, an econometric warning alert system needs to be reliable in revealing inflationary upturns in the market -- with a low false detection rate to avoid unnecessary policy measures and a high positive detection rate to assure appropriate policy implementation. The nonlinear structure of bubble phenomena typically diminishes the discriminatory power of test mechanisms. These power reductions complicate attempts at econometric dating and enhance the need for new approaches that do not suffer from this problem. One of the challenges of the current project is to develop and test such methods so that they may be used in an active policy environment. A second challenge lies in the application of these methods to the ballooning sovereign debt and credit default swap market, especially in the European Union periphery. Empirical applications are planned to analyze the European debt crisis using credit default swap spreads and to track migration of the phenomena through the financial system and real economies.In addition to the main branch of research on crisis econometrics the project will develop automated methods for systems of high dimensional time series allowing for co-movement and linkages among the variables as well as nonstationarity in the data. Massive recent improvements in the availability of electronic data offer new opportunities for statistical analysis including the use of very high dimensional datasets in practical work. Modern econometric practice now frequently encounters systems where the dimensionality of the variables may exceed the sample size. In such cases sparse statistical methods can be useful in resolving degrees of freedom problems, but that methodology is presently developed only for linear systems with stationary regressors. Econometric work in finance and macroeconomics typically involves nonstationary and potentially co-moving data series. These features introduce challenging nonlinearities and identification issues that must be addressed in the use of sparse estimation techniques. The second branch of this project pursues those extensions. Some related research in the project will consider large dimensional dynamic panels where an unknown subset of series have a common feature or parameter such as an autoregressive unit root. The new methodology will provide a data-determined approach to subset classification in regression, where there is some commonality in the regression characteristics across certain individuals in the panel. This type of econometric classification covers many empirical examples of interest such as convergence clubs that arise in global and regional economic growth analysis. Research on these panel classification devices will substantially extend the range of existing shrinkage methods and their potential empirical applications in economics.

金融史表明，资产价格泡沫、危机和恐慌是世界金融市场间歇性但却长期存在的特征。 2007-2009年全球金融危机（GFC）和当前主权债务危机的经验强调了这些历史教训，并见证了金融不稳定给实体经济活动和就业带来的代价。关于资本要求的新监管举措和巴塞尔协议 III 国际金融协议强调需要加强对银行和金融市场的监管，以帮助避免通常伴随资产价格泡沫的“过度信贷创造”。这种市场监督的一个重要的实际问题涉及对现有证据中“过度”的评估。央行经济学家和监管机构无法努力抵消投机泡沫，除非他们能够评估投机泡沫是否存在。这一难题的一个突出例子发生在 20 世纪 90 年代互联网泡沫的早期阶段，该泡沫在更短的时间内创造并摧毁了 8 万亿美元的股东财富。超过十年。 1996 年，艾伦·格林斯潘 (Alan Greenspan) 发表了著名的“非理性繁荣”言论，表达了对可能的资产价格通胀的担忧，但没有支持计量经济证据，也没有对市场升级或随后的崩盘产生影响。同样，在 2000 年代初，经济学家罗伯特·希勒 (Robert Shiller) 多次警告房价过度上涨，但未能提醒政策制定者注意正在出现的房地产投机泡沫。计量经济学工作可以通过量化与房地产相关的市场过度行为来协助监测金融市场的复杂活动。基本面。首席研究员（PI）的研究提供了金融繁荣和市场压力的预警系统。中央银行研究和监测团队已采用PI的一些方法来加强对金融资产、房地产和大宗商品价格市场的监测。这项工作涉及计量经济学分析，重点关注个人金融时间序列所揭示的属性以及数据中是否存在繁荣。当前的 NSF 项目扩展了检测技术的工作，提供了预期的约会算法，可以帮助监管机构进行市场监测活动，这些活动随着时间的推移和跨不同市场存在金融蔓延和危机连锁的风险。基于证据的警告诊断对于市场参与者和监管机构来说都是有用的警报机制。但为了有效地进行金融监督和监管工作，计量经济预警警报系统必须能够可靠地揭示市场通胀的好转——具有较低的错误检测率，以避免不必要的政策措施，具有较高的积极检测率，以确保采取适当的政策执行。气泡现象的非线性结构通常会削弱测试机制的辨别能力。这些功率的降低使计量经济测年的尝试变得复杂，并增加了对不受此问题困扰的新方法的需求。当前项目的挑战之一是开发和测试这些方法，以便它们可以在积极的政策环境中使用。第二个挑战在于将这些方法应用于不断膨胀的主权债务和信用违约掉期市场，特别是在欧盟外围国家。计划进行实证应用，利用信用违约互换利差来分析欧洲债务危机，并跟踪金融体系和实体经济中现象的迁移。除了危机计量经济学研究的主要分支之外，该项目还将开发用于系统的自动化方法。高维时间序列允许变量之间的共同运动和联系以及数据的非平稳性。最近电子数据可用性的巨大改进为统计分析提供了新的机会，包括在实际工作中使用非常高维的数据集。现代计量经济学实践现在经常遇到变量维数可能超过样本量的系统。在这种情况下，稀疏统计方法可用于解决自由度问题，但该方法目前仅针对具有平稳回归量的线性系统而开发。金融和宏观经济学中的计量经济学工作通常涉及非平稳且潜在联动的数据系列。这些特征引入了具有挑战性的非线性和识别问题，在使用稀疏估计技术时必须解决这些问题。该项目的第二个分支致力于这些扩展。该项目中的一些相关研究将考虑大维动态面板，其中未知的系列子集具有共同的特征或参数，例如自回归单位根。新方法将为回归中的子集分类提供一种数据确定的方法，其中面板中某些个体的回归特征存在一些共性。这种类型的计量经济学分类涵盖了许多有趣的经验例子，例如全球和区域经济增长分析中出现的趋同俱乐部。对这些面板分类装置的研究将大大扩展现有收缩方法的范围及其在经济学中的潜在实证应用。