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AUTHOR: Georg von Graevenitz

ABSTRACT: We use online search data to predict car sales in the German and UK automobile industries. Search data subsume several distinct search motives, which are not separately observable. We develop a model linking search motives to observable search data and sales. The model shows that predictions of sales relying on observable search data as a proxy for pre-purchase search will be biased. We show how to remove the biases and estimate the effect of pre-purchase search on sales. To assist identification of this effect, we use the introduction of scrappage subsidies for cars in 2008/2009 as a quasi-natural experiment. We also show that online search data are (i) highly persistent over time, (ii) potentially subject to permanent shocks, and (iii) correlated across products, but to different extent. We address these challenges to estimation and inference by using recent econometric methods for large N, large T panels.

KEYWORDS: Online search, Google Trends, Serial correlation, Non-stationarity, Common Correlated Effects, Large Panels. 

CITATION: von Graevenitz, G., Helmers, C., Millot, V., & Turnbull, O. (2016) "Does Online Search Preditc Sales? Evidence from Big Data for Car Markets in Germany and the UK", CCP Working Paper 16-7

Policy Brief 16-7


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