We analyse whether, and when, a large set of Google search data can be useful to increase euro area GDP nowcasting accuracy once we control for information contained in official variables. 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 .
Alternative data sets are nowadays widely used for macroeconomic nowcasting together with new Machine Learning-based tools which often are applied without having a complete picture of their theoretical nowcasting properties. Against this background, this paper proposes a theoretically-funded nowcasting methodology allowing to incorporate alternative Google Search Data (GSD) among the predictors and combining targeted preselection, 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 of different 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 .
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We analyse whether, and when, a large set of Google search data can be useful to increase euro area GDP nowcasting accuracy once we control for information contained in official variables. To deal with these data we propose an estimator that combines variable pre-selection and
Alternative data sets are nowadays widely used for macroeconomic nowcasting together with new Machine Learning-based tools which often are applied without having a complete picture of their theoretical nowcasting properties. Against this background, this paper proposes a theoretically-funded nowcasting methodology allowing to incorporate alternative Google Search Data (GSD) among the predictors and combining targeted preselection,
R
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
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
R
, including Google search data in the information set improves nowcasting accuracyeven when official macroeconomic information is available
of different 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