Vector Autoregressive Models with Structural Changes in Regression Coefficients and in Variance-Covariance Matrices

By Jushan Bai. 2000

109 citations recorded by the source corpus.

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Citations in the corpus

Breaks in the Variability and Comovement of G-7 Economic Growth (2003, background): “If we know the number of breaks, Bai (2000) suggests estimating the parameters by maximizing the Gaussian pseudo-likelihood for and .18 We estimate and by maximizing the likelihood over all unique partitions and parameters .19 Bai (2000) shows that so long as the conditioning assumption is correct,…”
Estimating and Testing Structural Changes in Multivariate Regressions (2007, background): “[7] ––—(2000): “Vector Autoregressive Models with Structural Changes in Regression Coefficients and in Variance-Covariance Matrices,” Annals of Economics and Finance, 1, 303-339.”
Instability of Return Prediction Models (2006, unknown): “The Qu and Perron (2004) methodology extends results in Bai, Lumsdaine and Stock (1998) and Bai (2000) for detecting and estimating breaks in multivariate systems.”
A review of change point detection methods (2018, methodology): “Theoretical consistency guarantees hold even if the noise εt is correlated, for instance a movingaverage process [13, 115].”
Common breaks in means and variances for panel data (2010, methodology): “For any given N , it is known that k̂ = k0 + Op(1), see Bai et al. (1998) and Bai (2000).”
Dealing with Structural Breaks (2005, background): “Also, Bai (2000) considers the consistency, rate of convergence and limiting distribution of estimated break dates in a segmented stationary VAR model estimated again by QML when the breaks can occur in the parameters of the conditional mean, the covariance matrix of the error term or both.”
Estimating Structural Changes in Regression Quantiles (2011, background): “Bai et al. (1998), Bai (2000) and Qu and Perron (2007) extended the analysis to a system of equations.”
Inference for single and multiple change‐points in time series (2013, methodology): “Bai (2000, 2010) took advantage of the quasi-likelihood technique so as to estimate change-points not only in the mean vector but also in the variance–covariance matrix of multivariate observations.”
Selective review of offline change point detection methods (2020, methodology): “The resulting cost function cAR is able to detect shifts in the autoregressive coefficients of a non-stationary process [21, 70].”
Nonparametric estimation of structural change points in volatility models for time series (2005, background): “Bai (1994) also obtained similar results for a mean shift in linear processes, while Bai (2000) had similar results for Gaussian autoregression models.”