We study the localization of mean changes in high-dimensional vector autoregressions (VARs) when the autoregressive dynamics remain common across regimes. We develop a three-step procedure that first obtains preliminary change-point estimates from the raw series ignoring the dependence structure, then estimates a common sparse VAR using observations away from the estimated changes, and finally filters the series to refine the change point locations. For a single change point, we show that the preliminary localization rate depends directly on the strength of temporal dependence, whereas filtering removes this dependence penalty from the oracle rate. With an estimated filter, the refined rate additionally reflects the error in estimating the VAR dynamics and the relation between the change in the stationary mean and the corresponding change in the VAR intercept. We characterize these rates explicitly and establish conditions under which filtering improves localization. Because filtering near a change point uses lags from adjacent regimes, it also creates a short-lived distortion in the filtered mean; we characterize when this boundary effect is innocuous and develop a transition-corrected alternative. We extend the procedure to an unknown number of change points. Overlapping local windows are used to generate candidate locations, an information criterion removes redundant and spurious candidates and estimates the number of changes, and the retained locations are subsequently refined using the estimated VAR filter. We establish consistency for the estimated number of changes and simultaneous localization rates for their locations. Numerical experiments corroborate the localization gains and show robustness to moderate departures from common VAR dynamics. An application to a large panel of U.S. macroeconomic indicators identifies economically interpretable changes.

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