Following the recent enthusiasm for Machine Learning (ML) methods and widespread availability of big data, macroeconomic forecasting research gradually evolved further and further away from the traditional tightly specified OLS regression. Rather, nonparametric non-linearity and regularization of many forms are slowly taking the center stage, largely because they can provide sizable forecasting gains with respect to traditional methods (see, among others, Kim and Swanson (2018); Medeiros et al. (2019); Goulet Coulombe et al. (2020); Goulet Coulombe (2020a)). In such environments, different linear transformations of the informational set X can change the prediction and taking first differences may not be the optimal transformation for many predictors, despite the fact that it guarantees viable frequentist inference. For instance, in penalized regression problems - like Lasso or Ridge, different rotations of X imply different priors on β in the original regressor space.