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的间歇性但长期存在的特征。2007-2009年全球金融危机和当前主权债务危机的经验强调了这些历史教训，并证明了金融不稳定在真实的经济活动和就业方面的代价。有关资本金要求的新监管举措以及《巴塞尔III》（雅阁协议III）都强调，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出现的“过度信贷创造”。这种市场监督的一个重要的实际问题是评估现有证据中什么是“过度”。中央银行的经济学家和监管者无法抵消投机泡沫，除非他们能够评估泡沫是否存在。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，在不到十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）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，但没有支持的计量经济学证据，也没有对市场升级或随后的崩盘产生影响。同样，在21世纪初，经济学家罗伯特·席勒（Robert Shiller）一再警告房价过度上涨，但未能提醒政策制定者注意房地产投机泡沫的出现。计量经济学可以通过量化市场与基本面的关系来帮助这种复杂的金融市场监测工作。首席研究员的研究提供了一个金融繁荣和市场压力的预警系统。PI的一些方法已被中央银行研究和监督小组采用，以加强对金融资产、真实的房地产和商品价格市场的监测。这项工作涉及计量经济学分析，侧重于个人金融时间序列的显示属性和存在的繁荣的数据。目前的NSF项目扩展了检测技术的工作，以提供预期的约会算法，可以帮助监管机构在市场监测活动中，随着时间的推移和不同市场的金融传染和危机连锁风险。循证预警诊断作为市场参与者和监管机构的预警机制是有用的。但是，为了有效地进行金融监督和监管工作，计量经济学预警系统需要在揭示市场通货膨胀上升方面具有可靠性-低误报率，以避免不必要的政策措施，高阳性检出率，以确保适当的政策执行。气泡现象的非线性结构通常会降低测试机制的辨别能力。这些功率降低使计量经济学测年的尝试复杂化，并增加了对不受此问题影响的新方法的需求。本项目的挑战之一是开发和测试这些方法，以便在积极的政策环境中使用。第二个挑战是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，特别是在欧洲联盟外围。实证应用程序计划分析欧洲债务危机使用信用违约掉期利差和跟踪迁移的现象，通过金融系统和真实的economics.In除了危机计量经济学的主要分支研究该项目将开发自动化的方法，系统的高维时间序列允许的共同运动和变量之间的联系，以及非平稳性的数据。最近电子数据可用性的大规模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系统中的变量的维数可能超过样本大小。在这种情况下，稀疏统计方法可以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开发。金融和宏观经济学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。这些功能引入了具有挑战性的非线性和识别问题，必须解决在使用稀疏估计技术。本项目的第二个分支将致力于这些扩展。本计画中的相关研究将考虑大维度动态面板，其中未知序列的子集具有共同的特征或参数，例如自回归单位根。新的方法将提供一个数据确定的方法来进行回归中的子集分类，其中在小组中的某些个体的回归特征中存在一些共性。这类计量经济学分类涵盖了许多令人感兴趣的实证例子，如全球和区域经济增长分析中出现的趋同俱乐部。对这些面板分类方法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.