Co-Breaking
协同结构突变(Co-breaking)AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要围绕协同结构突变的概念、回归与秩检验展开,并讨论它对政策分析和脉冲响应的影响,直接对应结构变化方向。
协同结构突变(Co-breaking)AI 解读 ↓
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This article has two aims. First, we provide a synopsis of the literature on co-breaking that has developed in several, seemingly disconnected, strands. We establish a consistent terminology, collect theoretical results, delimit co-breaking to cointegration and common features, and review recent contributions to co-breaking regressions and the budding analysis of co-breaking rank. Second, we present new results in the field, particularly, on the importance of co-breaking for policy analysis, with special emphasis on impulse-response functions. Moreover, a new procedure for co-breaking rank testing is presented, evaluated by Monte Carlo experiments, and illustrated using U.K. macroeconomic data.
协整系统中平滑结构变化的检验
This paper develops an econometric framework for analysing smooth structural change in cointegrated systems following a known intervention time. We consider a vector error-correction model in which the cointegration rank and the pre-intervention cointegrating structure are identified from a stable pre-intervention subsample. After the intervention, both the adjustment coefficients and the cointegrating vectors are allowed to evolve smoothly as functions of rescaled time,…
是否存在人工智能泡沫?亢奋期的稳健起止时间识别
The recent surge in valuations among AI related firms has renewed concerns that markets may be entering a new phase of speculative exuberance, especially in the technology and semiconductor sectors at the center of the AI investment wave. This paper develops a practical econometric framework for detecting, date-stamping, and drawing inference on the origination and collapse of bubble episodes when prices evolve under persistent, time-varying volatility. Standard bubble tests…
时变向量误差修正模型:估计与推断
This paper considers a time-varying vector error-correction model that allows for different time series behaviors (e.g., unit-root and locally stationary processes) to interact with each other and co-exist. From a practical perspective, this framework can be used to estimate shifts in the predictability of non-stationary variables, and test whether economic theories hold periodically, etc. We first develop a time-varying Granger Representation Theorem, which facilitates the…
面板数据单位根检验综述
This review discusses methods of testing for a panel unit root. Modern approaches to testing in cross-sectionally correlated panels are discussed, preceding the analysis with an analysis of independent panels. In addition, methods for testing in the case of non-linearity in the data (for example, in the case of structural breaks) are presented, as well as methods for testing in short panels, when the time dimension is small and finite. In conclusion, links to existing…
结构不稳定下的面板数据协整检验
Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the article.The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components.We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran's common correlated effects approach.This…
结构突变的 KS 型检验:基于调整极差的自归一化方法
A popular self-normalization (SN) approach in time series analysis uses the variance of a partial sum as a self-normalizer. This is known to be sensitive to irregularities such as persistent autocorrelation, heteroskedasticity, unit roots and outliers. We propose a novel SN approach based on the adjusted-range of a partial sum, which is robust to these aforementioned irregularities. We develop an adjusted-range based Kolmogorov–Smirnov type test for structural breaks for…