A general panel break test based on the self-normalization method

Abstract

We propose new break tests for parameters such as mean, variance, quantile and others of panel data sets, in a general setup based on the self-normalization method. The self-normalization tests show much better size than existing tests, resolving their over-size problem for panels with serial dependence, cross-sectional dependence, conditional heteroscedasticity and/or N relative larger than T, which is demonstrated theoretically by a nuisance parameter free limiting null distribution and experimentally by very stable finite sample sizes. The proposed test is also implemented much more easily than the existing tests in that the proposed test needs no bandwidth selection for the long-run variance estimation and is computed very simply. Applications of the self-normalization test to the financial stock return and realized volatility indicate more toward absence of breaks of mean and/or variance than the existing tests which neglect cross-sectional correlation and other features apparent in the data sets.