Publicação
Leveraging google search queries to help predict house prices in Portugal
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Rodrigues, Paulo Manuel Marques | |
| dc.contributor.author | Sistovaris, Nicholas | |
| dc.date.accessioned | 2023-08-03T09:28:40Z | |
| dc.date.available | 2023-08-03T09:28:40Z | |
| dc.date.issued | 2023-01-13 | |
| dc.date.submitted | 2022-12-16 | |
| dc.description.abstract | 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. | pt_PT |
| dc.identifier.tid | 203312643 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/156222 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | UID/ECO/00124/2013 | pt_PT |
| dc.subject | Econometrics | pt_PT |
| dc.subject | Housing | pt_PT |
| dc.subject | Google trends | pt_PT |
| dc.subject | Forecasting | pt_PT |
| dc.subject | Error-correction model | pt_PT |
| dc.title | Leveraging google search queries to help predict house prices in Portugal | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics | pt_PT |
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