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Nowcasting GDP growth using Google trends

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Authors
Heikkinen, Joni
Date
2019
Discipline
TaloustiedeEconomics
Copyright
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.

 
Tässä Pro gradu -tutkielmassa tutkitaan Google Trends -aineiston kykyä nowcasting ennustaa Saksan ja Suomen talouskasvua eli bruttokansantuotetta (BKT). Nowcasting pyrkii ennustamaan nykyistä taloudellista tilannetta. Google Trends -aineisto kuvaa taas erilaisten Google-hakujen suosiota. Varhaisissa tutkimuksissa havaittiin, että Google Trends -data voi tuottaa tarkkoja ennusteita monille taloudellisille muuttujille, joista monet liittyvät BKT:hen. Tähän liittyen, Götz & Knetsch (2019) käyttivät Google Trends -dataa Saksan BKT:n nowcasting ennustamiseen. BKT on tärkeä taloudellinen muuttuja, jolla on huomattava julkistamisviive. Taloudelliset muutokset voivat kuitenkin tapahtua nopeasti ja yllättävästi, ja siksi on tärkeää saada ajankohtaisempaa tietoa talouden tilasta. Google-hakudatan lisäksi tässä Pro gradu -tutkielmassa käytetään vertailukohteena kuluttajien luottamus -aineistoa. Tämä tutkielma seuraa Götz ja Knetsch (2019) tutkimusta ja valitsee samat alustavat hakukategoriat. Hakukategorioiden suuri lukumäärä aiheuttaa korkeaulotteisen aineiston ongelman. Tutkielma ratkaisee korkeaulotteisen aineiston ongelman käyttämällä sekä ulottuvuuden supistamis- että muuttujan valikointi -menetelmiä. Tutkimuskysymykseen vastatakseen Pro gradu -tutkielma luo nowcasting-ennusteharjoituksen, joka pyrkii simuloimaan todellista ennustetilannetta. Ennusteharjoituksessa käytettiin lukuisia ennustemalleja, joita vertailtiin niiden keskineliövirheen neliöjuurilla ja kuviolla. Tämän Pro gradu -tutkielman tulosten mukaan tarkin laajan Google-hakukategoriamalli oli ”Uutiset”-malli. Suomen tarkimmaksi alakategoriaksi paljastui ”Pankkitoiminta”-alakategoria. Saksassa taas ”Automessut”-kategoria oli tarkin alakategoria. Google-mallit toimivat paremmin Saksassa kuin Suomessa, jossa kuluttajien luottamus - aineisto tuotti johdonmukaisesti tarkempia ennusteita. Parhaimpia malleja arvioitiin myös ristiinvalidoinnilla, joka vahvisti aikaisemmat tulokset, ts. molemmissa maissa kuluttajien luottamus oli tarkin nowcasting-malli. Donadelli (2015) havaitsi, että Google- hauilla olisi yhteys politiikkaan liittyvään taloudelliseen epävarmuuteen. Tämä Pro gradu -tutkielma ei kuitenkaan havainnut yhtä vahvaa yhteyttä. ...
 
This master’s thesis examines Google Trends ability to nowcast Germany and Finland’s economic growth, i.e. gross domestic product (GDP). Nowcasting aims to forecast the current economic situation. Google Trends data reflects the popularity of different Google searches. Early studies found that Google Trends can generate accurate forecasts for various economic variables, many of which are related to GDP. In this regard, Götz and Knetsch (2019) used Google Trends data to nowcast Germany’s GDP. GDP is an important economic variable that is published quarterly and has a significant publication delay. However, economic changes can occur quickly and suddenly. Therefore, it is important to obtain up-to-date information about the current economic situation. In addition to Google Trends data, this study uses Germany and Finland’s consumer confidence data as a benchmark. This master’s thesis follows Götz and Knetsch’ (2019) study closely and selects similar initial search categories. A large number of initial search categories causes the problem of high dimensionality. This thesis solves the problem by using both dimension reduction and variable selection methods. The master’s thesis answers to the research topic by creating a nowcasting exercise that attempts to simulate a real-life nowcasting situation. Exercise will include multiple nowcasting models, which this thesis examines with their root mean square errors and figures. According to the results of this master’s thesis, the most accurate model for a broad Google category model was the “News” model. The models were also examined in sub- category levels. The “Banking” model was the most precise subcategory model in Finland. In Germany, however, the “Vehicle Shows” category was the most accurate subcategory. Overall, Google models perform significantly better in Germany than in Finland, where consumer confidence data provided very accurate predictions. Moreover, the thesis evaluated leading models with a leave-one-out cross-validation method, which confirmed previous results, i.e. in both countries, the consumer confidence was the leading model. Furthermore, Donadelli (2015) found that Google searches had a relationship with policy-related uncertainty. This study did not find a similar relationship. ...
 
Keywords
nowcasting forecasting Google Trends talouskasvu bruttokansantuote Google hakuohjelmat ennusteet economic growth gross domestic product search engines forecasts
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http://urn.fi/URN:NBN:fi:jyu-201911144867

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