ArticleJournal of the American Statistical Association2022
Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Sequential Change Point Detection in Dynamic Non-Stationary Manufacturing Processes.INFORMS journal on data science · 2026Article
- A longitudinal analysis of T1-weighted MRI features associated with progression from mild cognitive impairment to Alzheimer's disease.BMC medical imaging · 2026Article
- Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City.Transportation research record · 2023Article
- Granger Causality: A Review and Recent Advances.Annual review of statistics and its application · 2022Article
- Penalized estimation of threshold auto-regressive models with many components and thresholds.Electronic journal of statistics · 2022Article
- Causal Discovery in High-Dimensional Point Process Networks with Hidden Nodes.Entropy (Basel, Switzerland) · 2021Article
- Core community structure recovery and phase transition detection in temporally evolving networks.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to assume piecewise stationarity, where the model can change at potentially many change points. We propose a three-stage procedure for simultaneous estimation of change points and parameters of high-dimensional piecewise vector autoregressive (VAR) models. In the first step, we reformulate the change point detection problem as a high-dimensional variable selection one, and solve it using a penalized least square estimator with a total variation penalty. We show that the penalized estimation method over-estimates the number of change points, and propose a selection criterion to identify the change points. In the last step of our procedure, we estimate the VAR parameters in each of the segments. We prove that the proposed procedure consistently detects the number and location of change points, and provides consistent estimates of VAR parameters. The performance of the method is illustrated through several simulated and real data examples.
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Registered trials
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