What question did this study set out to answer?

Develop asymptotic statistical theory and hypothesis tests for detecting and estimating multiple unknown structural change points in linear regression models.

January 1, 1998EconometricaOpen Access

Estimating and Testing Linear Models with Multiple Structural Changes

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Authors

JBJushan BaiColumbia University PPPierre PerrónBoston University

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Overview

Methodological study demonstrates sequential estimation of multiple change points in time series regression models, highlighting robust detection of structural shifts.

Key Points

Develop asymptotic statistical theory and hypothesis tests for detecting and estimating multiple unknown structural change points in linear regression models.
Formulated theoretical frameworks evaluating both fixed and shrinking magnitude parameter shifts in full and partial structural change models.
Incorporated serially correlated error disturbances modeled as mixingale processes.
Designed a sequential estimation algorithm that detects break points successively rather than through simultaneous global optimization.
Derived the rate of convergence and limiting distributions for the estimated break dates and regression coefficients.
Established test statistics capable of determining both the presence and the exact number of structural breaks in time series data.

Cite This Study

Bai et al. (1998) studied this question.

synapsesocial.com/papers/69d7cf39a2a48916bbbedc22 https://doi.org/10.2307/2998540

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January 1, 1998EconometricaOpen Access

Estimating and Testing Linear Models with Multiple Structural Changes

View Full Paper

Ask AI

Bookmark

Authors

JBJushan BaiColumbia University PPPierre PerrónBoston University

Discussion

Member takes

Overview

Methodological study demonstrates sequential estimation of multiple change points in time series regression models, highlighting robust detection of structural shifts.

Key Points

Develop asymptotic statistical theory and hypothesis tests for detecting and estimating multiple unknown structural change points in linear regression models.
Formulated theoretical frameworks evaluating both fixed and shrinking magnitude parameter shifts in full and partial structural change models.
Incorporated serially correlated error disturbances modeled as mixingale processes.
Designed a sequential estimation algorithm that detects break points successively rather than through simultaneous global optimization.
Derived the rate of convergence and limiting distributions for the estimated break dates and regression coefficients.
Established test statistics capable of determining both the presence and the exact number of structural breaks in time series data.

Cite This Study

Bai et al. (1998) studied this question.

synapsesocial.com/papers/69d7cf39a2a48916bbbedc22 https://doi.org/10.2307/2998540

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