Publicação
Leveraging google search queries to help predict house prices in Portugal
| Resumo: | This work project contributes to the current literature on using Google search queries to predict economic activity. We demonstrate, using the two-step Error-Correction Model (ECM) by Engle and Granger (1987), that specific search queries, also known as Google Trends, are related to house prices in Portugal. For out-of-sample forecasts, our ECM model with the Google Trends variables performed significantly better predicting one year ahead, in which, the Mean Absolute Error was reduced by over 30% compared to our baseline model. Until now, conventional economics has not leveraged this highly accessible digital data in their models, we hope this will change. |
|---|---|
| Autores principais: | Sistovaris, Nicholas |
| Assunto: | Econometrics Housing Google trends Forecasting Error-correction model |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868984166082674688 |
|---|---|
| author | Sistovaris, Nicholas |
| author_facet | Sistovaris, Nicholas |
| author_role | author |
| contributor_name_str_mv | Rodrigues, Paulo Manuel Marques RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Sistovaris, Nicholas\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Rodrigues, Paulo Manuel Marques RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Sistovaris, Nicholas |
| datacite.date.Accepted.fl_str_mv | 2023-01-13T00:00:00Z |
| datacite.date.available.fl_str_mv | 2023-08-03T09:28:40Z |
| datacite.date.embargoed.fl_str_mv | 2023-08-03T09:28:40Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Econometrics Housing Google trends Forecasting Error-correction model |
| datacite.titles.title.fl_str_mv | Leveraging google search queries to help predict house prices in Portugal |
| dc.contributor.none.fl_str_mv | Rodrigues, Paulo Manuel Marques RUN |
| dc.creator.none.fl_str_mv | Sistovaris, Nicholas |
| dc.date.Accepted.fl_str_mv | 2023-01-13T00:00:00Z |
| dc.date.available.fl_str_mv | 2023-08-03T09:28:40Z |
| dc.date.embargoed.fl_str_mv | 2023-08-03T09:28:40Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/156222 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Econometrics Housing Google trends Forecasting Error-correction model |
| dc.title.fl_str_mv | Leveraging google search queries to help predict house prices in Portugal |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | This work project contributes to the current literature on using Google search queries to predict economic activity. We demonstrate, using the two-step Error-Correction Model (ECM) by Engle and Granger (1987), that specific search queries, also known as Google Trends, are related to house prices in Portugal. For out-of-sample forecasts, our ECM model with the Google Trends variables performed significantly better predicting one year ahead, in which, the Mean Absolute Error was reduced by over 30% compared to our baseline model. Until now, conventional economics has not leveraged this highly accessible digital data in their models, we hope this will change. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/0096dc27-19f9-4647-a3c7-84f657bd5dac/download |
| id | run_27c6fa0d648d6fd76a030d066b2913f5 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/156222 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/156222 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Sistovaris, Nicholas |
| publishDate | 2023 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTThis work project contributes to the current literature on using Google search queries to predict economic activity. We demonstrate, using the two-step Error-Correction Model (ECM) by Engle and Granger (1987), that specific search queries, also known as Google Trends, are related to house prices in Portugal. For out-of-sample forecasts, our ECM model with the Google Trends variables performed significantly better predicting one year ahead, in which, the Mean Absolute Error was reduced by over 30% compared to our baseline model. Until now, conventional economics has not leveraged this highly accessible digital data in their models, we hope this will change.application/pdfpt_PTLeveraging google search queries to help predict house prices in PortugalSistovaris, NicholasRodrigues, Paulo Manuel MarquesHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2033126432023-08-03T09:28:40Z2023-01-132022-12-162023-01-13T00:00:00ZHandlehttp://hdl.handle.net/10362/156222http://purl.org/coar/access_right/c_abf2open accessEconometricsHousingGoogle trendsForecastingError-correction model1745867 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesishttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/0096dc27-19f9-4647-a3c7-84f657bd5dac/download |
| spellingShingle | Leveraging google search queries to help predict house prices in Portugal Sistovaris, Nicholas Econometrics Housing Google trends Forecasting Error-correction model |
| status | SINGLETON |
| subject.fl_str_mv | Econometrics Housing Google trends Forecasting Error-correction model |
| title | Leveraging google search queries to help predict house prices in Portugal |
| title_full | Leveraging google search queries to help predict house prices in Portugal |
| title_fullStr | Leveraging google search queries to help predict house prices in Portugal |
| title_full_unstemmed | Leveraging google search queries to help predict house prices in Portugal |
| title_short | Leveraging google search queries to help predict house prices in Portugal |
| title_sort | Leveraging google search queries to help predict house prices in Portugal |
| topic | Econometrics Housing Google trends Forecasting Error-correction model |
| topic_facet | Econometrics Housing Google trends Forecasting Error-correction model |
| url | http://hdl.handle.net/10362/156222 |
| visible | 1 |