Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data

Abstract

Internet search data could be a useful source of information for policymakers when formulating decisions based on their understanding of the current economic environment. This paper builds on earlier literature via a structured value assessment of the data provided by Google Trends. This is done through two empirical exercises related to the forecasting of changes in UK unemployment. Firstly, economic intuition provides the basis for search term selection, with a resulting Google indicator tested alongside survey-based variables in a traditional forecasting environment. Secondly, this environment is expanded into a pseudo-time nowcasting framework which provides the backdrop for assessing the timing advantage that Google data have over surveys. The framework is underpinned by a MIDAS regression which allows, for the first time, the easy incorporation of Internet search data at its true sampling rate into a nowcast model for predicting unemployment.

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

“…Several studies have suggested that Google trends data are a valuable economic indicator. Researchers have emphasized that Google Trends has strong potential for assessing unemployment rate changes in Germany (Askitas and Zimmermann, 2009), France (Fondeur and Karamé, 2013), Visegrad countries (Pavlicek and Kristoufek, 2015), the UK (Smith, 2016) and the US (D'Amuri and Marcucci, 2017). Goel et al (2010) examine, among other things, the relationship between the use of search engines and real estate sales, as well as disease prevalence.…”

Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data

“…Many scholars have recently worked on the idea to bridge this gap by using Big Data (BD), retrievable from the web almost in real time and with the granularity level needed by local policy makers (Mariani et al, 2018;Smith, 2016;Song & Liu, 2017).…”

“…Many scholars have recently worked on the idea to bridge this gap by using Big Data (BD), retrievable from the web almost in real time and with the granularity level needed by local policymakers (Mariani et al, 2018; Smith, 2016; Song and Liu, 2017). Specifically, web search queries and climate statistics have been exploited to augment the micro-founded econometric specifications of tourism demand models that embody prices and income as main explanatory variables (Li et al, 2017b; Zhang and Kulendran, 2017).…”

Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data

“…We follow a different approach and exploit the Google topic unemployment to retrieve, separately for each country, the top-25 level-1 queries and the top-10 level-2 queries in the original language in the period January 2015 -December 2019. This data-driven approach is similar to the use of a list of root keywords in Da et al (2015) and Smith (2016) to retrieve the associated queries. Our root, however is not a single keyword or a list of keywords, but the language-independent topic.…”