Forecasting Private Consumption: Survey-Based Indicators vs. Google Trends

By T. Schmidt, Simeon Vosen. 2009

601 citations recorded by the source corpus.

In this study we introduce a new indicator for private consumption based on search query time series provided by Google Trends. The indicator is based on factors extracted from consumption-related search categories of the Google Trends application Insights for Search. The forecasting performance of the new indicator is assessed relative to the two most common survey-based indicators - the University of Michigan Consumer Sentiment Index and the Conference Board Consumer Confidence Index. The results show that in almost all conducted in-sample and out-of-sample forecasting experiments the Google indicator outperforms the survey-based indicators. This suggests that incorporating information from Google Trends may offer significant benefits to forecasters of private consumption.

Subfields: Economics and Econometrics, Epidemiology, Marketing

View Paper: https://www.semanticscholar.org/paper/fb1c803596e867b23748b5e1ec4fb3d2ccad41e5

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Citations in the corpus

Can Google Data Help Predict French Youth Unemployment (2012, methodology): “…apply a factor model on a large set of Google queries to extract principal components that improve nowcasts of US private consumption (see also Schmidt and Vosen (2009) for a comparison between Google indicators and the usual survey-based indicators for the US, and see Suhoy (2010) on…”
Now-Casting and the Real-time Data Flow (2013, unknown): “…high frequency data, such as financial, which we have mentioned in the text, research on the potential value of other sources of information like Google search 39 volumes (see for example Vosen and Schmidt, 2011) is also interesting and we are sure will be the object of future investigation.”
Forecasting Chinese tourist volume with search engine data (2015, background): “…of tweets, website traffic, and social media posts to forecast contagious disease outbreaks (Carneiro & Mylonakis, 2009), consumer consumption (Vosen & Schmidt, 2011), popularity of songs and movies (Goel, Hofman, Lahaie, Pennock, & Watts, 2010), unemployment rates (Askitas & Zimmermann,…”
Ten years of research change using Google Trends: From the perspective of big data utilizations and applications (2017, background): “To overcome these limitations, there have been more attempts to create an index to make relative comparisons that take account of other variables or attempts to use this data in conjunction with other sources of big data (Mavragani and Tsagarakis, 2016; Vosen and Schmidt, 2011).”
Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review (2018, methodology): “The monitoring of Web-based activity is a valid indicator of public behavior, and it has been effectively used in predictions [18,19], nowcastings [20], and forecasting [17,21,22].”