Estimating and Testing Linear Models with Multiple Structural Changes

TLDR

The study develops statistical theory and proposes test statistics for detecting and estimating multiple change points in regression models. The authors analyze a partial structural change model with fixed or shrinking shifts, serially correlated disturbances, and introduce a successive estimation strategy that does not require simultaneous determination of break locations. They derive the rate of convergence and limiting distribution for the estimated change‑point parameters.

Abstract

This paper develops the statistical theory for testing and estimating multiple change points in regression models. The rate of convergence and limiting distribution for the estimated parameters are obtained. Several test statistics are proposed to determine the existence as well as the number of change points. A partial structural change model is considered. The authors study both fixed and shrinking magnitudes of shifts. In addition, the models allow for serially correlated disturbances (mixingales). An estimation strategy for which the location of the breaks need not be simultaneously determined is discussed. Instead, the authors' method successively estimates each break point.