On the sensitivity of unit root inference to nonlinear data transformations
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In this paper we analyze the sensitivity of unit root inference to nonlinear transformations through Bayesian techniques. We make joint inference about the Box-Cox transformation, which includes the cases yt and log(yt), and the unit root. When we apply our method to the 14 Nelson-Plosser series, we find that unit root inference can be very sensitive to the transformation chosen and that the usual practice of taking logs is not always warranted.