Multiple breaks detection in general causal time series using penalized quasi-likelihood

DOI10.1214/12-EJS680zbMath1337.62210OpenAlexW2904240262MaRDI QIDQ1950823

Jean-Marc Bardet, Olivier Wintenberger, William Charky Kengne

Publication date: 28 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1332162336

zbMATH Keywords

quasi-maximum likelihood estimatorchange detectionAR(\(\infty\)) processesARCH(\(\infty\)) processescausal processesmodel selection by penalized likelihood

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Non-Markovian processes: hypothesis testing (62M07)

Related Items (17)

On change-points tests based on two-samples \(U\)-statistics for weakly dependent observations ⋮ Piecewise autoregression for general integer-valued time series ⋮ Inference and model selection in general causal time series with exogenous covariates ⋮ Monitoring procedure for parameter change in causal time series ⋮ Testing for parameter constancy in general causal time-series models ⋮ Strongly consistent model selection for general causal time series ⋮ Contrast estimation of time-varying infinite memory processes ⋮ Epidemic change-point detection in general causal time series ⋮ Consistent model selection criteria and goodness-of-fit test for common time series models ⋮ Two tests for sequential detection of a change-point in a nonlinear model ⋮ A Cramér-von Mises test for a class of mean time dependent CHARN models with application to change-point detection ⋮ On consistency for time series model selection ⋮ Data-driven semi-parametric detection of multiple changes in long-range dependent processes ⋮ Inference and testing for structural change in general Poisson autoregressive models ⋮ Autocovariance Estimation in Regression with a Discontinuous Signal and m‐Dependent Errors: A Difference‐Based Approach ⋮ A Kernel Multiple Change-point Algorithm via Model Selection ⋮ Inference for single and multiple change-points in time series

Uses Software

• CAPUSHE

Cites Work

• Slope heuristics: overview and implementation
• Asymptotic normality of the quasi-maximum likelihood estimator for multidimensional causal processes
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• Weakly dependent chains with infinite memory
• Estimating the number of change-points via Schwarz' criterion
• GARCH processes: structure and estimation
• Change-point estimation in ARCH models
• The multiple change-points problem for the spectral distribution
• Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes
• Testing for a change in the parameter values and order of an autoregressive model
• Pseudo-maximum likelihood estimation of \(\text{ARCH}(\infty)\) models
• Least‐squares Estimation of an Unknown Number of Shifts in a Time Series
• A test for a change in a parameter occurring at an unknown point
• Break Detection for a Class of Nonlinear Time Series Models
• Estimating and Testing Linear Models with Multiple Structural Changes
• An Inequality and Almost Sure Convergence
• Inference about the change-point in a sequence of random variables

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