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. |
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| 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 |
| 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. |
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