Do Google Searches Help in Nowcasting Private Consumption? A Real-Time Evidence for the US

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractIn 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 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.

Google Internet search activity and volatility prediction in the market for foreign currency

“…Da et al (2011) find a link between Google searches and stock trading volume. Vosen and Schmidt (2011) and Kholodilin et al (2010) use Google search volume data to forecast private consumption expenditures. Kulkarni et al (2009) use Google search volume data to predict housing prices.…”

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Health and well-being in the great recession

“…The official data then serves as the benchmark against which to check the reporting. The data has been used for consumption research (Kholodilin et al, 2010;Vosen, 2009, 2010), housing prices (Kulkarni et al, 2009), unemployment (Askitas andZimmermann, 2009;D'Amuri and Marcucci, 2010), as well as finance (Sims, 2010) and policy (Bersier, 2010). It has also been used to enhance the performance of more traditional forecasting models as in Kholodilin et al (2009).…”