Crisis Econometrics and High Dimensional Nonstationary Regression

金融历史表明，资产价格泡沫、危机和恐慌是世界金融市场断断续续但长期存在的特征。2007-2009年全球金融危机和当前主权债务危机的经验突出了这些历史教训，并见证了金融不稳定在实体经济活动和就业方面的代价。针对资本金要求的新监管举措以及《巴塞尔协议III》强调，有必要加强对银行和金融市场的监督，以帮助避免通常伴随资产价格泡沫而来的“过度信贷创造”。在这种市场监督中，一个重要的实际问题涉及评估现有证据中什么是“过度”的。除非央行经济学家和监管机构能够评估投机泡沫是否存在，否则他们无法努力抵消投机泡沫。最近一个突出的例子发生在20世纪90年代互联网泡沫的早期阶段，那次泡沫在不到10年的时间里创造并摧毁了8万亿美元的股东财富。1996年，艾伦·格林斯潘(Alan Greenspan)曾说过一句著名的话：“非理性繁荣”，他表达了对可能的资产价格通胀的担忧，但没有计量经济学证据支持，也没有对市场升级或随后的崩盘产生任何影响。同样，在本世纪初，经济学家罗伯特·希勒(Robert Shiller)一再警告房价过度上涨，但未能提醒政策制定者注意正在形成的房地产投机泡沫。计量经济学研究可以通过量化市场相对于基本面的过度行为，帮助这一复杂的金融市场监测工作。首席调查员(PI)的研究为金融繁荣和市场压力提供了一个预警系统。央行研究和监督小组已经采用了PI的一些方法，以加强对金融资产、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，重点是单个金融时间序列的揭示属性和数据中存在的繁荣。目前的NSF项目扩展了检测技术方面的工作，以提供前瞻性的约会算法，可以帮助监管机构在市场监控活动中提供帮助，在市场监控活动中，随着时间的推移，跨不同市场存在金融传染和危机连锁的风险。基于证据的警告诊断对于市场参与者和监管者来说都是有用的警报机制。但为了在金融监督和监管工作中发挥有效作用，计量经济学警报系统需要能够可靠地揭示市场上的通胀回升--低的误检率可以避免不必要的政策措施，高的阳性检测率可以确保适当的政策实施。气泡现象的非线性结构通常会削弱测试机制的辨别能力。这些电力削减使计量经济学年代测定的尝试复杂化，并增强了对不受这一问题影响的新方法的需求。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。第二个挑战在于如何将这些方法应用于不断膨胀的主权债务和信用违约互换市场，尤其是在欧盟外围国家。该项目计划使用信用违约互换利差来分析欧洲债务危机，并跟踪这些现象在金融体系和实体经济中的迁移。除了危机计量经济学研究的主要分支外，该项目还将为高维时间序列系统开发自动化方法，允许变量之间的协同移动和联系，以及数据中的非平稳性。最近在电子数据可获得性方面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度的数据集。现代计量经济学实践现在经常遇到变量的维度可能超过样本大小的系统。在这种情况下，稀疏统计方法在解决自由度问题时是有用的，但该方法目前仅适用于具有平稳回归变量的线性系统。金融和宏观经济学中的计量经济学工作通常涉及非平稳和潜在的共同移动的数据序列。这些特征引入了在使用稀疏估计技术时必须解决的具有挑战性的非线性和识别问题。该项目的第二个分支负责这些扩展。该项目中的一些相关研究将考虑大型动态面板，其中未知的级数子集具有共同的特征或参数，如自回归单位根。新的方法将为回归中的子集分类提供一种数据确定的方法，其中小组中的某些个人的回归特征有一些共性。这种类型的计量经济学分类涵盖了许多感兴趣的经验例子，例如全球和区域经济增长分析中出现的趋同俱乐部。对这些板材分类装置的研究将极大地扩展现有收缩方法的范围及其在经济学中的潜在经验应用。

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.