Adaptive Unit Root Inference in Autoregressions using the Lasso Solution Path

We show that the activation knot of a potentially non-stationary regressor on the adaptive Lasso solution path in autoregressions can be leveraged for selection-free inference about a unit root. The resulting test has asymptotic power against local alternatives in $1/T$ neighbourhoods, unlike post-selection inference methods based on consistent model selection. Exploiting the information enrichment principle devised by Reinschl\"ussel and Arnold arXiv:2402.16580 [stat.ME] to improve the Lasso-based selection of ADF models, we propose a composite statistic and analyse its asymptotic distribution and local power function. Monte Carlo evidence shows that the combined test dominates the comparable post-selection inference methods of Tibshirani et al. [JASA, 2016, 514, 600-620] and may surpass the power of established unit root tests against local alternatives. We apply the new tests to groundwater level time series for Germany and find evidence rejecting stochastic trends to explain observed long-term declines in mean water levels.