On consistency of minimum description length model selection for piecewise autoregressions

DOI10.1016/j.jeconom.2016.05.013zbMath1443.62250OpenAlexW2419600008MaRDI QIDQ308393

Stacey A. Hancock, Yi-Ching Yao, Richard A. Davis

Publication date: 6 September 2016

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2016.05.013

zbMATH Keywords

model selectionminimum description lengthstructural breakchange-pointautoregressive process

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items

Piecewise autoregression for general integer-valued time series, Consistency of minimum description length model selection for piecewise stationary time series models

Uses Software

• wbs

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Wild binary segmentation for multiple change-point detection
• Markov chains and stochastic stability
• Estimation of a noisy discrete-time step function: Bayes and empirical Bayes approaches
• An MDL approach to the climate segmentation problem
• A note on strong mixing of ARMA processes
• Estimating the number of change-points via Schwarz' criterion
• Time series: theory and methods.
• Likelihood ratio tests for multiple structural changes
• Change-point estimation in ARCH models
• Fitting multiple change-point models to data
• Estimating the locations of multiple change points in the mean
• Testing for a change in the parameter values and order of an autoregressive model
• The functional law of the iterated logarithm for stationary strongly mixing sequences
• Nonparametric multiple change-point estimators
• Consistency of minimum description length model selection for piecewise stationary time series models
• Testing for change points in time series models and limiting theorems for NED sequences
• Break Detection for a Class of Nonlinear Time Series Models
• Mixing properties of harris chains and autoregressive processes
• Estimating the Number of Change Points in Exponential Families Distributions
• Testing and Locating Variance Changepoints with Application to Stock Prices
• Estimating and Testing Linear Models with Multiple Structural Changes
• Bayesian Retrospective Multiple-Changepoint Identification
• Group LASSO for Structural Break Time Series
• A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data
• Estimating the Current Mean of a Normal Distribution which is Subjected to Changes in Time
• Structural Break Estimation for Nonstationary Time Series Models
• An estimator of the number of change points based on a weak invariance principle
• Long memory and regime switching