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Do Google search queries contain relevant information on job separation?: a mixed-frequency modelling approach

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posted on 29.03.2022, 01:28 by Samir Sultani
There is a growing interest in alternative sources of data within the economic literature. These sources are referred to as 'Big Data'. Internet search queries are one such data source, providing an avenue to discern the real-time behaviour of users searching for information online. This thesis explores whether weekly Google search query data contain informationally-relevant signals on the composition of the US labour market; that is, search queries seemingly employed by Internet users in fear of losing their jobs, or planning to quit the labour force altogether, for example 'unemployment insurance'. A weekly composite search index is constructed from the search query data, in order to utilise all the data available in the given period. The relationship between Google search and the unemployment rate is modelled using a recently developed technique in the mixed-frequency time series literature to model the weekly Google search data and the corresponding job separations data, specifically, Ghysels et al's (2015) model. To assess the informational content of Internet search query data, a mixed-frequency Granger causality test is conducted. It is established that there is insuffcient evidence to suggest that Internet search query data is useful in predicting future job separation statistics.


Alternative Title

Information content of Google searches on job separation.

Table of Contents

1. Introduction -- 2. Literature review -- 3. Methodology -- 4. Results and discussion -- 5. Conclusion.


Bibliography: pages 61-68 Theoretical thesis. Spine title: The information content of Google searches on job separation.

Awarding Institution

Macquarie University

Degree Type

Thesis MRes


MRes, Macquarie University, Faculty of Business and Economics, Department of Economics

Department, Centre or School

Department of Economics

Year of Award


Principal Supervisor

Chris Heaton

Additional Supervisor 1

Edwin Franks


Copyright Samir Sultani 2015. Copyright disclaimer: http://www.copyright.mq.edu.au




United States


1 online resource (68 pages)

Former Identifiers

mq:46281 http://hdl.handle.net/1959.14/1083934