--- title: "Big Data: Google Searches Predict Unemployment in Finland" authors: - name: "Tuhkuri, Joonas" url: "https://www.etla.fi/henkilot/tuhkuri-joonas/" sameAs: - "https://twitter.com/joonastuhkuri" - "https://joonastuhkuri.com/" date_published: "2014-08-14" series: "ETLA Raportit - Reports" series_number: "31" issn: - "2323-2447" - "2323-2455" jel_codes: - "C1" - "C22" - "C43" - "C53" - "C82" - "E27" language: "fi" publisher: "ETLA Economic Research" pdf_url: "https://www.etla.fi/wp-content/uploads/ETLA-Raportit-Reports-31.pdf" identifier: "https://www.etla.fi/julkaisut/id/ETLA-Raportit---Reports-31" abstract_translations: en: "There are over 3 billion searches globally on Google every day. This report examines whether Google search queries can be used to predict the present and the near future unemployment rate in Finland. Predicting the present and the near future is of interest, as the official records of the state of the economy are published with a delay. To assess the information contained in Google search queries, the report compares a simple predictive model of unemployment to a model that contains a variable, Google Index, formed from Google data. In addition, cross-correlation analysis and Granger-causality tests are performed. Compared to a simple benchmark, Google search queries improve the prediction of the present by 10 % measured by mean absolute error. Moreover, predictions using search terms perform 39 % better over the benchmark for near future unemployment 3 months ahead. Google search queries also tend to improve the prediction accuracy around turning points. The results suggest that Google searches contain useful information of the present and the near future unemployment rate in Finland." --- # Big Data: Google Searches Predict Unemployment in Finland **Published:** 2014-08-14 **Categories:** Publikationer, Rapporter **URL:** https://www.etla.fi/sv/publikationer/33195/ #### Abstract There are over 3 billion searches globally on Google every day. This report examines whether Google search queries can be used to predict the present and the near future unemployment rate in Finland. Predicting the present and the near future is of interest, as the official records of the state of the economy are published with a delay. To assess the information contained in Google search queries, the report compares a simple predictive model of unemployment to a model that contains a variable, Google Index, formed from Google data. In addition, cross-correlation analysis and Granger-causality tests are performed. Compared to a simple benchmark, Google search queries improve the prediction of the present by 10 % measured by mean absolute error. Moreover, predictions using search terms perform 39 % better over the benchmark for near future unemployment 3 months ahead. Google search queries also tend to improve the prediction accuracy around turning points. The results suggest that Google searches contain useful information of the present and the near future unemployment rate in Finland. --- ## Additional Information **Issn:** 2323-2447, 2323-2455 (Pdf) **Jel:** C1, C22, C43, C53, C82, E27 **Kansikuva:** https://www.etla.fi/wp-content/uploads/ETLA-Raportit-Reports-31-kansi.jpg **Key Words:** Big Data, Google, Internet, Nowcasting, Forecasting, Unemployment, Time-series analysis **Kieli:** fi **Kirjoittajat:** Tuhkuri, Joonas **Lataa Pdf:** https://www.etla.fi/wp-content/uploads/ETLA-Raportit-Reports-31.pdf **Paivays:** 14.08.2014 **Saatavuus:** 2 **Sarja:** ETLA Raportit - Reports **Sarja Nro:** 31 **Sivuja:** 36 http://www.etla.fi/etlanow/ **Publication Research:** No ## Bibliographic metadata (JSON-LD) ```json { "@context": "https://schema.org", "@type": "ScholarlyArticle", "@id": "https://www.etla.fi/julkaisut/id/ETLA-Raportit---Reports-31", "headline": "Big Data: Google Searches Predict Unemployment in Finland", "inLanguage": "fi", "publisher": { "@type": "Organization", "name": "ETLA Economic Research", "url": "https://www.etla.fi", "logo": { "@type": "ImageObject", "url": "https://www.etla.fi/wp-content/uploads/logo-etla_logo.svg" } }, "datePublished": "2014-08-14T00:00:00+00:00", "author": [ { "@type": "Person", "name": "Tuhkuri, Joonas", "url": "https://www.etla.fi/henkilot/tuhkuri-joonas/", "sameAs": [ "https://twitter.com/joonastuhkuri", "https://joonastuhkuri.com/" ] } ], "abstract": "", "keywords": [ { "@type": "DefinedTerm", "name": "Big Data", "@language": "en" }, { "@type": "DefinedTerm", "name": "Google", "@language": "en" }, { "@type": "DefinedTerm", "name": "Internet", "@language": "en" }, { "@type": "DefinedTerm", "name": "Nowcasting", "@language": "en" }, { "@type": "DefinedTerm", "name": "Forecasting", "@language": "en" }, { "@type": "DefinedTerm", "name": "Unemployment", "@language": "en" }, { "@type": "DefinedTerm", "name": "Time-series analysis", "@language": "en" } ], "isPartOf": { "@type": "PublicationIssue", "issueNumber": "31", "isPartOf": { "@type": "Periodical", "name": "ETLA Raportit - Reports", "issn": [ "2323-2447", "2323-2455" ] } }, "mainEntityOfPage": "https://www.etla.fi/sv/publikationer/33195/", "url": "https://www.etla.fi/sv/publikationer/33195/", "hasPart": { "@type": "CreativeWork", "inLanguage": "en", "text": "There are over 3 billion searches globally on Google every day. This report examines whether Google search queries can be used to predict the present and the near future unemployment rate in Finland. Predicting the present and the near future is of interest, as the official records of the state of the economy are published with a delay. To assess the information contained in Google search queries, the report compares a simple predictive model of unemployment to a model that contains a variable, Google Index, formed from Google data. In addition, cross-correlation analysis and Granger-causality tests are performed. Compared to a simple benchmark, Google search queries improve the prediction of the present by 10 % measured by mean absolute error. Moreover, predictions using search terms perform 39 % better over the benchmark for near future unemployment 3 months ahead. Google search queries also tend to improve the prediction accuracy around turning points. 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