Arbeitspapier
Estimating the number of mean shifts under long memory
Detecting the number of breaks in the mean can be challenging when it comes to the long memory framework. Tree-based procedures can be applied to time series when the location and number of mean shifts are unknown and estimate the breaks consistently though with possible overfitting. For pruning the redundant breaks information criteria can be used. An alteration of the BIC, the LWZ, is presented to overcome long-range dependence issues. A Monte Carlo Study shows the superior performance of the LWZ to alternative pruning criteria like the BIC or LIC.
- Language
-
Englisch
- Bibliographic citation
-
Series: Diskussionsbeitrag ; No. 496
- Classification
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Subject
-
long memory
mean shift
regression tree
ART
LWZ
LIC.
- Event
-
Geistige Schöpfung
- (who)
-
Sibbertsen, Philipp
Willert, Juliane
- Event
-
Veröffentlichung
- (who)
-
Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
- (where)
-
Hannover
- (when)
-
2012
- Handle
- Last update
- 10.03.2025, 11:45 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Sibbertsen, Philipp
- Willert, Juliane
- Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
Time of origin
- 2012
Other Objects (12)
On variance estimation under shifts in the mean
On scale estimation under shifts in the mean
Mean Ergodic Weighted Shifts on Köthe Echelon Spaces
On variance estimation under shifts in the mean
Regime shifts in mean-variance efficient frontiers: Some international evidence
Fast and efficient image novelty detection based on mean-shifts
Fast and Efficient Image Novelty Detection Based on Mean-Shifts
Estimating trends in the global mean temperature record
Long Memory and Level Shifts: Re-Analyzing Inflation Rates
Level shifts and long memory: A state space approach
Neutrosophic ratio-type estimators for estimating the population mean
Estimating the Mean Direction of Strongly Dependent Circular Time Series
On variance estimation under shifts in the mean
On scale estimation under shifts in the mean
Mean Ergodic Weighted Shifts on Köthe Echelon Spaces
On variance estimation under shifts in the mean
Regime shifts in mean-variance efficient frontiers: Some international evidence
Fast and efficient image novelty detection based on mean-shifts
Fast and Efficient Image Novelty Detection Based on Mean-Shifts
Estimating trends in the global mean temperature record
Long Memory and Level Shifts: Re-Analyzing Inflation Rates
Level shifts and long memory: A state space approach
Neutrosophic ratio-type estimators for estimating the population mean
Estimating the Mean Direction of Strongly Dependent Circular Time Series
On variance estimation under shifts in the mean
On scale estimation under shifts in the mean
Mean Ergodic Weighted Shifts on Köthe Echelon Spaces
On variance estimation under shifts in the mean
Regime shifts in mean-variance efficient frontiers: Some international evidence
Fast and efficient image novelty detection based on mean-shifts
Fast and Efficient Image Novelty Detection Based on Mean-Shifts
Estimating trends in the global mean temperature record
Long Memory and Level Shifts: Re-Analyzing Inflation Rates
Level shifts and long memory: A state space approach
Neutrosophic ratio-type estimators for estimating the population mean