In this thesis, the usefulness of search engine data to nowcast the unemployment rate of Sweden is evaluated. Four different indices from Google Trends based on keywords related to unemployment are used in the analysis and six different regARIMA models are estimated and evaluated. The results indicate that the fit is improved for models when data from Google Trends is included. To evaluate the nowcast ability of models, one-step-ahead predictions are calculated. Although the prediction error is lower for the models with data from Google Trends, Diebold-Mariano tests do not indicate that the predictions are significantly better compared topredictions from a model without data from Google Trends. It is therefore concluded that one cannot state that data from Google Trends improves nowcasts of the unemployment rate of Sweden. Additionally, predictions are calculated for longer forecast horizons. This analysis indicates that Google search data could be useful to forecast the unemployment rate of longerforecast horizons.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-505464 |
| Date | January 2023 |
| Creators | Inganäs, Jacob |
| Publisher | Uppsala universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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