  Alternative data sets are widely used for macroeconomic nowcasting together
with machine learning--based tools. The latter are often applied without a
complete picture of their theoretical nowcasting properties. Against this
background, this paper proposes a theoretically grounded nowcasting methodology
that allows researchers to incorporate alternative Google Search Data (GSD)
among the predictors and that combines targeted preselection, Ridge
regularization, and Generalized Cross Validation. Breaking with most existing
literature, which focuses on asymptotic in-sample theoretical properties, we
establish the theoretical out-of-sample properties of our methodology and
support them by Monte-Carlo simulations. We apply our methodology to GSD to
nowcast GDP growth rate of several countries during various economic periods.
Our empirical findings support the idea that GSD tend to increase nowcasting
accuracy, even after controlling for official variables, but that the gain
differs between periods of recessions and of macroeconomic stability.
