Panel data segmentation under finite time horizon

DOI10.1016/J.JSPI.2015.05.007zbMath1326.62081arXiv1501.00177OpenAlexW1920879871MaRDI QIDQ897629

Leonid Torgovitski

Publication date: 7 December 2015

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1501.00177

zbMATH Keywords

CUSUMpanel datasegmentationnonparametricLassoserial dependencechange point estimationtotal variation denoising

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Sequential statistical analysis (62L10)

Related Items (3)

Break point detection for functional covariance ⋮ Panel data segmentation under finite time horizon ⋮ Structural breaks in panel data: Large number of panels and short length time series

Cites Work

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• Common breaks in means and variances for panel data
• Panel data segmentation under finite time horizon
• Maximum likelihood estimation in the multi-path change-point problem
• Regularized estimation of large covariance matrices
• Extensions of some classical methods in change point analysis
• Testing structural changes in panel data with small fixed panel size and bootstrap
• Structural breaks in time series
• Estimation in multi-path change-point problems
• Common breaks in time trends for large panel data with a factor structure
• Change‐point detection in panel data
• Multiscale Change Point Inference

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