Template-Type:  ReDIF-Paper 1.0
Series:  Tinbergen Institute Discussion Papers
Creation-Date: 03/15/2019
Number: 19-021/III
Author-Name: Robin Niesert
Author-Workplace-Name: 
Author-Name: Jochem Oorschot
Author-Workplace-Name: Econometric Institute, Erasmus University
Author-Name: Chris Veldhuisen
Author-Name: Kester Brons
Author-Name: Rutger-Jan Lange
Author-Workplace-Name: Econometric Institute, Erasmus University
Title: 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 out- of-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).
Classification-JEL: C11, C53
Keywords: Bayesian methods, forecasting practice, Kalman filter, macroeconomic forecasting, state space models, nowcasting, spike-and-slab, Hamiltonian sampler
File-Url: https://papers.tinbergen.nl/19021.pdf
File-Format:  application/pdf
File-Size:  470831 bytes
Handle:  RePEc:tin:wpaper:20190021