The 2007–2008 U.S. Recession: What Did the Real‐time Google Trends Data Tell the United States?

Chen, Tao; So, Erin P. K.; Wu, Liang; Yan, Isabel K.

doi:10.1111/coep.12074

Cited by 31 publications

(15 citation statements)

References 28 publications

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“…We aimed to assess the income level in a nation by looking at the source of income via data on queries for “job”, “foreclosure” and “layoff”. Studies like Chen, So, Wu, and Yan (), Webb (), and Choi and Varian () used these phrases to get a sense of the labor market in the economy.…”

Section: Empirical Methodology and Datamentioning

confidence: 99%

“…In a related paper,Vosen and Schmidt (2011) showed that the forecasting performance of the Google Trends indicator is as good as those provided by the University of Michigan Consumer Sentiment Index and the Conference Board Consumer Confidence Index. In her real-time inflation expectation measure,Guzman (2011) showed that high-frequency measures using Google Trends can outperform the low-frequency measures provided by survey data.5 Chen et al (2015) used Google search volume data and successfully detected the most recent US business cycle peak within a month after the actual turning point in the economy.6 Da, Engelberg, and Gao (2011) used search volume data as a measure of investor attention Kristoufek (2013). found a significant connection between search queries and the value of the Bitcoin.7 Maasoumi and Bulut (2012) provided evidence of specification problems for linear structural models.…”

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confidence: 99%

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Google Trends and the forecasting performance of exchange rate models

Bulut

2017

Journal of Forecasting

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In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.

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Section: Empirical Methodology and Datamentioning

confidence: 99%

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confidence: 99%

Google Trends and the forecasting performance of exchange rate models

Bulut

2017

Journal of Forecasting

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“…Investigating business cycles is one wellresearched area: Askitas and Zimmermann (2013) demonstrate that the German business cycle can be nowcasted by highway Toll data. Chen et al (2015) find that Google search volume data help improve the timeliness of business cycle turning point identification during the 2007-2008 US recession.…”

Section: Other Forecasting Nowcasting and Proxyingmentioning

confidence: 98%

The internet as a data source for advancement in social sciences

Askitas

Zimmermann

2015

International Journal of Manpower

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents inIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. This paper advocates the use of Internet data for social sciences with a special focus on human resources issues. It discusses the potentials and challenges of Internet data for social sciences and presents a selection of the relevant literature to establish the wide spectrum of topics, which can be reached. Such data represent a large and increasing part of everyday life, which cannot be measured otherwise. They are timely, perhaps even daily following the factual process, they typically involve large numbers of observations, and they allow for flexible conceptual forms and experimental settings. Internet data can successfully be applied to a very wide range of human resource issues including forecasting (e.g. of unemployment, consumption goods, tourism, festival winners and the like), nowcasting (obtaining relevant information much earlier than through traditional data collection techniques), detecting health issues and well-being (e.g. flu, malaise and ill-being during economic crises), documenting the matching process in various parts of individual life (e.g. jobs, partnership, shopping), and measuring complex processes where traditional data have known deficits (e.g. international migration, collective bargaining agreements in developing countries). Major problems in data analysis are still unsolved and more research on data reliability is needed. Current research is highly original but also exploratory and premature. Our article reviews the current attempts in the literature to incorporate Internet d...

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“…Preis et al (2013) relate Google queries to stock market dynamics and show that losses are often preceded by a growing volume of specific stock market search terms. In a recent publication, Chen et al (2015) evaluate to what extent Google search queries can be used to "now-cast" business cycle turning points during 2007-2008. Schmidt and Vossen (2012) use Google Trends to account for special events in economic forecasting.…”

Section: Empirical Applicationsmentioning

confidence: 99%

Prospects for utilisation of non-vacancy Internet data in labour market analysis—an overview

2016

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Along with the advancement of the Internet in the last decade, researchers have increasingly identified the web as a research platform and a data source, pointing out its value for labour market analysis. This article presents a review of online data sources for this field. Specifically, the article introduces web-based research, focusing on the potential of relatively new data sources such as Google Trends, social networks (LinkedIn, Facebook and Twitter) and Glassdoor (surveys). For these data sources, a review is done and recent empirical applications are listed. Web-based data can further our understanding of the dynamics of the labour market.

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The 2007–2008 U.S. Recession: What Did the Real‐time Google Trends Data Tell the United States?

Cited by 31 publications

References 28 publications

Google Trends and the forecasting performance of exchange rate models

Google Trends and the forecasting performance of exchange rate models

The internet as a data source for advancement in social sciences

Prospects for utilisation of non-vacancy Internet data in labour market analysis—an overview

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