Testing for nonlinearity in mean in the presence of heteroskedasticity

This paper considers an important practical problem in testing time-series data for nonlinearity in mean, namely, the distortion in the size of the test encountered if the the data are heteroskedastic. It is shown that using a heteroskedastic consistent auxiliary regression together with the wild bootstrap is an e®ective way of dealing with the problem. Simulation results indicate that signi¯cant improvements in empirical size are obtained, particularly in small samples.

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