Identification and Testing Structural Changes at Unknown Locations with Wavelets

用小波识别和测试未知位置的结构变化

• 批准号: RGPIN-2014-04170
• 负责人: Gencay, Ramazan
• 金额: $ 1.02万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636025
• 关键词: Identification; Testing; Structural; Changes; Unknown

Identification and Testing Structural Changes at Unknown Locations with WaveletsThe primary goal of my research program is to design powerful statistical tests that can be used to identify structural breaks (changes) in the parameters of a linear regression model in small samples. A structural break is an unexpected shift in the mean, persistence and/or in the variance of the underlying statistical process. The presence of such breaks leads to prediction errors and mis-specified models of economic and financial activity. A structural break may affect some or all parameters of a model. Some, for instance, may lead into an altered serial correlation after a certain date, some change in the propensity of a trend and some may lead into a change in the underlying volatility. Institutional changes (reforms), political turmoil, emergence of new economies, economic and financial disasters may account for the presence of such structural breaks in the economic and financial time series. Some breaks may be readily identifiable from raw data, but some may be latent because of the nature of the frictions and volatile nature of the economy. In a regression framework, the breaks account for the change in the nature of the relationship between two or more variables altogether. The wavelet transformation is an ideal toolset for the design of powerful structural change tests because it operates in local time windows and has powerful time/frequency localization features. The mechanism of my original wavelet framework can be described in the following way: The wavelet decomposition yields localized weighted differences (wavelet coefficients) that are equal in number to the number of data points. I square each wavelet coefficient to obtain its magnitude and calculate the sample average of the squared coefficients. I expect this sample average to be equal to one-half of the variance plus one-half of the covariance of the process under the null when there is no structural change. However, the sample average of the squared wavelet coefficients should be significantly deviate from the null average when there are one or more structural breaks. I center and standardize the sample average of the squared wavelet coefficients to obtain the null distribution with no structural change.Structural breaks can be permanent or temporary in nature. If a structural break is permanent, then there is a permanent change of indefinite duration in the mean, persistence or variance of the process. In a temporary break, the mean, persistence or the variance shifts from its null value but eventually reverts to its null value. Whether such breaks are temporary or permanent in nature, they may occur abruptly or gradually. To capture such possibilities, I will allow for abrupt as well as gradual smooth structural breaks. I focus on smooth multiple structural breaks for two reasons. First, most economic and financial data exhibit gradual structural changes in a time window, and the most abrupt structural changes are exceptions rather than the rule. Second, my framework is all-encompassing and allows for abrupt changes. My seed Monte Carlo simulations indicate the presence of minimal empirical size distortions relative to their nominal sizes and significant power improvements compared to the existing structural break tests.Identification of structural changes in an economic activity is a pre-cursor to avoid erroneous policies and to design proper remedies. The diagnosis of such changes requires tests with empirical size close to its nominal counterpart and substantive power, otherwise we may not be able to identify the underlying relationship correctly and in a timely manner. The goal of my research program is to overcome such limitations by providing powerful tests with m

识别和测试未知位置的结构变化与小波我的研究计划的主要目标是设计强大的统计测试，可用于识别小样本线性回归模型参数的结构突变（变化）。结构性突变是指潜在统计过程的均值、持续性和/或方差的意外变化。这种突变的存在会导致经济和金融活动的预测错误和错误指定的模型。结构突变可能会影响模型的部分或全部参数。例如，有些可能会导致在某个日期之后改变序列相关性，有些可能会导致趋势倾向的变化，有些可能会导致潜在波动性的变化。体制变化（改革）、政治动荡、新经济体的出现、经济和金融灾难可能是经济和金融时间序列中出现这种结构性突变的原因。有些中断可能很容易从原始数据中识别出来，但由于摩擦的性质和经济的不稳定性，有些中断可能是潜在的。在回归框架中，突变解释了两个或多个变量之间关系性质的变化。小波变换是一个理想的工具集，设计强大的结构变化测试，因为它在当地的时间窗口，并具有强大的时/频局部化功能。我的原始小波框架的机制可以用以下方式描述：小波分解产生的局部加权差异（小波系数）在数量上等于数据点的数量。我平方每个小波系数，以获得其幅度，并计算平方系数的样本平均值。我期望这个样本平均值等于零值下过程的方差的一半加上协方差的一半，当没有结构变化时。然而，当存在一个或多个结构突变时，平方小波系数的样本平均值应显著偏离零平均值。对小波系数平方的样本平均值进行中心化和标准化，以获得无结构变化的零分布。如果结构突变是永久性的，那么在过程的均值、持续性或方差中就有一个不确定持续时间的永久性变化。在一个暂时的中断中，均值、持续性或方差从其空值移动，但最终恢复到其空值。无论这种中断是暂时性的还是永久性的，它们可能突然发生，也可能逐渐发生。为了抓住这种可能性，我将考虑突然的和逐渐平滑的结构性断裂。我之所以关注平滑的多重结构性断裂，有两个原因。首先，大多数经济和金融数据在一个时间窗口内表现出渐进的结构变化，最突然的结构变化是例外而不是规则。第二，我的框架包罗万象，允许突然的变化。我的种子蒙特卡罗模拟表明存在最小的经验规模扭曲相对于其名义规模和显着的权力改善相比，现有的结构性断裂tests.Identification的经济活动中的结构性变化是一个前体，以避免错误的政策，并设计适当的补救措施。对这种变化的诊断需要检验的经验规模接近其名义对应物和实质力量，否则我们可能无法正确和及时地识别潜在的关系。我的研究计划的目标是通过提供强有力的测试来克服这些局限性。