Willert, Juliane (2009): Mean Shift detection under long-range dependencies with ART.
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Atheoretical regression trees (ART) are applied to detect changes in the mean of a stationary long memory time series when location and number are unknown. It is shown that the BIC, which is almost always used as a pruning method, does not operate well in the long memory framework. A new method is developed to determine the number of mean shifts. A Monte Carlo Study and an application is given to show the performance of the method.
|Item Type:||MPRA Paper|
|Original Title:||Mean Shift detection under long-range dependencies with ART|
|Keywords:||long memory, mean shift, regression tree, ART, BIC|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
|Depositing User:||Juliane Willert|
|Date Deposited:||16. Oct 2009 06:56|
|Last Modified:||19. Feb 2013 08:22|
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