Do Google searches help in nowcasting private consumption? A real-time evidence for the US

Arbeitspapier

In this paper, we investigate whether the Google search activity can help in nowcasting the year-on-year growth rates of monthly US private consumption using a real-time data set. The Google-based forecasts are compared to those based on a benchmark AR(1) model and the models including the consumer surveys and financial indicators. According to the Diebold-Mariano test of equal predictive ability, the null hypothesis can be rejected suggesting that Google-based forecasts are significantly more accurate than those of the benchmark model. At the same time, the corresponding null hypothesis cannot be rejected for models with consumer surveys and financial variables. Moreover, when we apply the test of superior predictive ability (Hansen, 2005) that controls for possible data-snooping biases, we are able to reject the null hypothesis that the benchmark model is not inferior to any alternative model forecasts. Furthermore, the results of the model confidence set (MCS) procedure (Hansen et al., 2005) suggest that the autoregressive benchmark is not selected into a set of the best forecasting models. Apart from several Google-based models, the MCS contains also some models including survey-based indicators and financial variables. We conclude that Google searches do help improving the nowcasts of the private consumption in US.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: KOF Working Papers ; No. 256

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Forecasting Models; Simulation Methods ; Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
• Subject: Google indicators ; real-time nowcasting ; principal components ; US private consumption ; Gesamtwirtschaftlicher Konsum ; Prognoseverfahren ; Suchmaschine ; Informationsverhalten ; Konsumentenverhalten ; USA

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Kholodilin, Konstantin A. ; Podstawski, Maximilian ; Siliverstovs, Boriss
• Event: Veröffentlichung
• (who): ETH Zurich, KOF Swiss Economic Institute
• (where): Zurich
• (when): 2010

Further information

• DOI: doi:10.3929/ethz-a-006070977
• Handle: http://hdl.handle.net/10419/50431
• Last update: 10.03.2025, 11:45 AM CET