Before revision:  To deal with these data we propose an estimator that combines variable pre-selection and Ridge regularization and study its theoretical properties. We show that in a period of cyclical stability Google data convey useful information for real-time nowcasting of GDP growth at the beginning of the quarter when macroeconomic information is lacking. On the other hand, in periods that exhibit a sudden downward shift in GDP growth rate , including Google search data in the information set improves nowcasting accuracyeven when official macroeconomic information is available .
After revision:  To deal with these data we propose an estimator that combines variable pre-selection and Ridge regularization and Generalized Cross Validation. Breaking with most of the existing literature that focuses on asymptotic in-sample theoretical properties, we establish the theoretical out-of-sample properties of our methodology, that are supported by Monte-Carlo simulations. We apply our methodology to GSD in order to nowcast GDP growth rate , including Google search data in the information set improves nowcasting accuracyeven when official macroeconomic information is available .