Predicting Recessions in Real-Time: Mining Google Trends and Electronic Payments Data for Clues
28 Pages Posted: 8 Sep 2013
Date Written: September 4, 2013
Many official economic indicators are released with a time lag, released infrequently and often require revision. In this Commentary, I discuss new sources of electronically recorded data that are both timely and reflect the real-time intentions of millions (or billions) of agents. Specifically, I consider whether Google searches and the growth of electronic payments variables, such as debit and credit card transactions, would have predicted the 2008-2009 recession.
Not too long ago, Canadian empirical macroeconomic researchers would have to wait two months for the release of the monthly National Accounts in order to update their models and forecasts. However, in the last 10 years to 20 years, technological innovations have resulted in vast amounts of other data being recorded electronically and stored. New data series are now being generated at a rate faster than analysts can study them.
Due to the emergence of Google as the dominant search engine, its search-term usage can provide a snapshot of current group interests in numerous issues, such as economics, politics, health, etc. In principle, if many people are entering the same economic search terms, this could provide a clue about changing conditions, such as the onset of a recession.
I find that the usage of Google search terms “recession” and “jobs” could have predicted the recession up to three months in advance of its onset. However, since Google query data are only available from 2004, the time span studied in this Commentary is very short in the context of business cycles. Consequently, our study should be viewed as illustrative of the potential uses of electronic data. I also highlight the benefits and pitfalls that users of Google data may encounter in the context of economic monitoring.
Keywords: Google, web engine, Financial Services, Macroeconomic Indicators
JEL Classification: E30
Suggested Citation: Suggested Citation