We carry out a meta-analysis on the frequency of unit-roots in macroeconomic time series with a dataset covering 249 variables for the G7 countries. We use linear tests and the three popular non-linear tests (TAR, ESTAR and Markov Switching). In general, the evidence in favour of the random walk hypothesis is weaker than in previous studies. This evidence against unit roots is stronger for real and nominal asset prices. Our results show that rejection of the null of a unit root in the macro dataset is substantially higher for non-linear than linear models. Finally, the results from a Monte Carlo experiment show that rejection frequencies are very close to the nominal size of the test when the DGP is a linear unit root process. This leads us to reject the hypothesis that overfitting deterministic components explains the higher rejection frequencies of nonlinear tests
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