Journal Article10.2307/2171955
Monitoring structural change
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TL;DR: In this article, the authors propose and analyze two real-time monitoring procedures with controlled size asymptotically: the fluctuation and CUSUM monitoring procedures, and extend an invariance principle in the sequential testing literature to obtain their results.
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Abstract: Contemporary tests for structural change deal with detections of the one-shot type: given an historical data set of fixed size, these tests are designed to detect a structural break within the data set. Due to the law of the iterated logarithm, one-shot tests cannot be applied to monitor out-of-sample stability each time new data arrive without signalling a nonexistent break with probability one. We propose and analyze two real-time monitoring procedures with controlled size asymptotically: the fluctuation and CUSUM monitoring procedures. We extend an invariance principle in the sequential testing literature to obtain our results. Simulation results show that the proposed monitoring procedures indeed have controlled asymptotic size. Detection timing depends on the magnitude of parameter change, the signal to noise ratio, and the location of the out-of-sample break point.
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Citations
strucchange. An R package for testing for structural change in linear regression models.
TL;DR: An R package called strucchange makes powerful tools available to display information about structural changes in regression relationships and to assess their significance and it is described how incoming data can be monitored.
Testing for multiple bubbles: historical episodes of exuberance and collapse in the s&p 500
TL;DR: In this paper, a recursive flexible window method was proposed for detecting and dating financial bubbles in real-time data, which is better suited for practical implementation with long historical time series.
Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500
TL;DR: In this article, a new recursive flexible window method that is better suited for practical implementation with long historical time series is developed. But the method is a generalized version of the sup ADF test of Phillips, Wu and Yu (2011, PWY) and delivers a consistent date-stamping strategy for the origination and termination of multiple bubbles.
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Strucchange: An R package for testing for structural change in linear regression models
TL;DR: Strucchange as discussed by the authors is a R package for testing structural change in linear regression models. But it is not suitable for the analysis of regression relationships and does not have the ability to detect structural changes in regression relationships.
693
Near real-time disturbance detection using satellite image time series
TL;DR: In this article, a multi-purpose time-series-based disturbance detection approach is proposed to identify and model stable historical variation to enable change detection within newly acquired data, which can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds and does not require time series gap filling.
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References
Tests for Parameter Instability and Structural Change with Unknown Change Point.
TL;DR: In this article, the authors considered tests for parameter instability and structural change with unknown change point, and the results apply to a wide class of parametric models that are suitable for estimation by generalized method of moments procedures.
Procedures for reacting to a change in distribution
TL;DR: In this paper, a problem of optimal stopping is formulated and simple rules are proposed which are asymptotically optimal in an appropriate sense, which is of central importance in quality control and also has applications in reliability theory.
Optimal stopping times for detecting changes in distributions
TL;DR: In this article, it was shown that Page's stopping time is optimal for the detection of changes in distributions, in a well defined sense, which is a generalization of an existing result where it was proved that Page stopping time was optimal asymptotically.
Multiple Time Series Regression with Integrated Processes
TL;DR: In this article, the authors developed a general asymptotic theory of regression for processes which are integrated of order one, including vector autoregressions and multivariate regressions among integrated processes that are driven by innovation sequences.