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We propose simple Kolmogorov–Smirnov- and Cramér–von Mises-type tests for symmetry of a continuous distribution around an unknown center. The tests are based on a characterization involving symmetric integration of the distribution function around its median. The asymptotic null distributions of the test statistics are derived in the linear regression setup where the goal is to test for symmetry of the error distribution, and the tests are shown to be asymptotically consistent against general alternatives. Numerical results show that the tests are level preserving and have competitive power when compared to existing classical and modern tests for symmetry. Moreover, for the considered alternatives, the new tests seem to have consistently higher empirical power than the corresponding classical Kolmogorov–Smirnov and Cramér–von Mises tests.

Tests for symmetry based on the integrated empirical process