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[{"key": "dc.contributor.advisor", "value": "Heimonen, Kari", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Juvonen, Petteri", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Heikkinen, Joni", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2019-11-14T09:02:49Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2019-11-14T09:02:49Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2019", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/66363", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Ta\u0308ssa\u0308 Pro gradu -tutkielmassa tutkitaan Google Trends -aineiston kykya\u0308 nowcasting ennustaa Saksan ja Suomen talouskasvua eli bruttokansantuotetta (BKT). Nowcasting pyrkii ennustamaan nykyista\u0308 taloudellista tilannetta. Google Trends -aineisto kuvaa taas erilaisten Google-hakujen suosiota. Varhaisissa tutkimuksissa havaittiin, etta\u0308 Google Trends -data voi tuottaa tarkkoja ennusteita monille taloudellisille muuttujille, joista monet liittyva\u0308t BKT:hen. Ta\u0308ha\u0308n liittyen, Go\u0308tz & Knetsch (2019) ka\u0308yttiva\u0308t Google Trends -dataa Saksan BKT:n nowcasting ennustamiseen. BKT on ta\u0308rkea\u0308 taloudellinen muuttuja, jolla on huomattava julkistamisviive. Taloudelliset muutokset voivat kuitenkin tapahtua nopeasti ja ylla\u0308tta\u0308va\u0308sti, ja siksi on ta\u0308rkea\u0308a\u0308 saada ajankohtaisempaa tietoa talouden tilasta.\n\nGoogle-hakudatan lisa\u0308ksi ta\u0308ssa\u0308 Pro gradu -tutkielmassa ka\u0308yteta\u0308a\u0308n vertailukohteena kuluttajien luottamus -aineistoa. Ta\u0308ma\u0308 tutkielma seuraa Go\u0308tz ja Knetsch (2019) tutkimusta ja valitsee samat alustavat hakukategoriat. Hakukategorioiden suuri lukuma\u0308a\u0308ra\u0308 aiheuttaa korkeaulotteisen aineiston ongelman. Tutkielma ratkaisee korkeaulotteisen aineiston ongelman ka\u0308ytta\u0308ma\u0308lla\u0308 seka\u0308 ulottuvuuden supistamis- etta\u0308 muuttujan valikointi -menetelmia\u0308. Tutkimuskysymykseen vastatakseen Pro gradu -tutkielma luo nowcasting-ennusteharjoituksen, joka pyrkii simuloimaan todellista ennustetilannetta. Ennusteharjoituksessa ka\u0308ytettiin lukuisia ennustemalleja, joita vertailtiin niiden keskinelio\u0308virheen nelio\u0308juurilla ja kuviolla.\n\nTa\u0308ma\u0308n Pro gradu -tutkielman tulosten mukaan tarkin laajan Google-hakukategoriamalli oli \u201dUutiset\u201d-malli. Suomen tarkimmaksi alakategoriaksi paljastui \u201dPankkitoiminta\u201d-alakategoria. Saksassa taas \u201dAutomessut\u201d-kategoria oli tarkin alakategoria. Google-mallit toimivat paremmin Saksassa kuin Suomessa, jossa kuluttajien luottamus - aineisto tuotti johdonmukaisesti tarkempia ennusteita. Parhaimpia malleja arvioitiin myo\u0308s ristiinvalidoinnilla, joka vahvisti aikaisemmat tulokset, ts. molemmissa maissa kuluttajien luottamus oli tarkin nowcasting-malli. Donadelli (2015) havaitsi, etta\u0308 Google- hauilla olisi yhteys politiikkaan liittyva\u0308a\u0308n taloudelliseen epa\u0308varmuuteen. Ta\u0308ma\u0308 Pro gradu -tutkielma ei kuitenkaan havainnut yhta\u0308 vahvaa yhteytta\u0308.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "This master\u2019s thesis examines Google Trends ability to nowcast Germany and Finland\u2019s 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, Go\u0308tz and Knetsch (2019) used Google Trends data to nowcast Germany\u2019s 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\u2019s consumer confidence data as a benchmark. This master\u2019s thesis follows Go\u0308tz and Knetsch\u2019 (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\u2019s 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\u2019s thesis, the most accurate model for a broad Google category model was the \u201cNews\u201d model. The models were also examined in sub- category levels. The \u201cBanking\u201d model was the most precise subcategory model in Finland. In Germany, however, the \u201cVehicle Shows\u201d 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.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2019-11-14T09:02:49Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2019-11-14T09:02:49Z (GMT). No. of bitstreams: 0\n Previous issue date: 2019", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "98", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "nowcasting", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "forecasting", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "Google Trends", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Nowcasting GDP growth using Google trends", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-201911144867", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Jyv\u00e4skyl\u00e4 University School of Business and Economics", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Jyv\u00e4skyl\u00e4n yliopiston kauppakorkeakoulu", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Taloustieteet", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Business and Economics", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Taloustiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Economics", "language": "en", "element": "subject", 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