Common breaks in means for panel data under short-range dependence

Abstract

In this paper, we revisit the model studied in Bai (2010), in which a common break in means for panel data with linear-process errors was studied. The errors admit the form of for series i. Bai (2010) conducted the change-point analysis under the situation where for all i. In this paper, we suppose and for all i, that is, the errors are short-range dependence. Then the least squares estimator of the common break is examined. In comparison to Bai (2010), the theoretical results in this paper reveal that: (1) Short-range dependence does not have the impact on the consistency of the estimator of the common break under the case of fixed magnitude of breaks. (2) It also does not have the impact on the convergence rate while it contaminates the limiting distribution of the estimator of the common break under the case of shrinking magnitude of breaks. Monte Carlo simulations are conducted to examine the finite-sample performance of the estimator. Our theoretical findings are supported by the Monte Carlo simulations.