Can Google search data help predict macroeconomic series?

Abstract

We use Google search data with the aim of predicting unemployment, CPI and consumer confidence for the US, UK, Canada, Germany and Japan. Google search queries have previously proven valuable in predicting macroeconomic variables in an in-sample context. To our knowledge, the more challenging question of whether such data have out-of-sample predictive value has not yet been satisfactorily answered. We focus on out-of-sample nowcasting, and extend the Bayesian Structural Time Series model using the Hamiltonian sampler for variable selection. We find that the search data retain their value in an outof-sample predictive context for unemployment, but not for CPI and consumer confidence. It may be that online search behaviour is a relatively reliable gauge of an individual's personal situation (employment status), but less reliable when it comes to variables that are unknown to the individual (CPI) or too general to be linked to specific search terms (consumer confidence).

Forecasting unemployment with Google Trends: age, gender and digital divide

“…In the last decade, this idea has produced a fruitful literature with, in particular, many papers focused on predicting the evolution of the labor market. Some examples are the studies by Varian (2009, 2012); Pavlicek and Kristoufek (2015); Niesert et al (2020) and Caperna et al (2020). Other papers, closer to ours, will be discussed in the next section.…”

Do search queries predict violence against women? A forecasting model based on Google Trends

“…More recently, Niesert et al (2019) obtained data from Google to forecast macroeconomic series, and they concluded that predictive power of the Internet queries is higher for those series that are directly linked to personal situations, such as employment status. There some other recent economic applications, such as Yu et al (2019), who demonstrated that forecasting models that include data from Google Trends outperform traditional ones when predicting oil consumption trend and level.…”

Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire

“…Further, survey data can be also available at a regional level. Recently, Google search data have been shown to have predictive power for unemployment (e.g., Caperna et al, 2022;Choi & Varian, 2009;D'Amuri & Marcucci, 2017;Fondeur & Karamé, 2013;Nagao et al, 2019;Niesert et al, 2020). Their main advantages are that they are free from revisions and can be obtained in specific geographical areas, such as the region of a country.…”