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.

Additional Metadata
Keywords Bayesian inference, logarithmic transformation, unit roots
JEL Time-Series Models; Dynamic Quantile Regressions (jel C22)
Persistent URL dx.doi.org/10.1016/S0165-1765(98)00014-7, hdl.handle.net/1765/2143
Journal Economics Letters
Citation
Franses, Ph.H.B.F, & Koop, G. (1998). On the sensitivity of unit root inference to nonlinear data transformations. Economics Letters, 7–15. doi:10.1016/S0165-1765(98)00014-7