Abstract
SummaryChanges in the distribution of income over time are identified based on an adjusted two‐sample version of the Neyman smooth test by using subsampling methods to approximate the sampling distribution of the test statistic when samples are not independent of each other. A range of Monte Carlo experiments show that the approach corrects for size distortions arising from dependent samples as well as generating monotonic power functions. Applying the approach to studying the distribution of income in Australia over the business cycle and the Global Financial Crisis, the empirical results highlight the importance of higher‐order moments and demonstrate that business cycles are not all alike as the relative strengths of higher‐order moments vary over phases of the cycle.
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