Abstract
The forecasting field has been using the surge in big data and advanced computational capabilities. This article discusses the methodological issues of Google Trends (GT) data reliability and forecasting validity for youth unemployment forecasts. We demonstrate the problems with static GT forecasting procedures and show a 44% increase in forecasting accuracy by applying time-varying model respecification forecasting.
| Original language | English |
|---|---|
| Title of host publication | 6th International Conference on Advanced Research Methods and Analytics, CARMA 2024 |
| Pages | 272-280 |
| Number of pages | 9 |
| DOIs | |
| Publication status | Published - Jul 2024 |
| Event | 6th International Conference on Advanced Research Methods and Analytics, CARMA 2024 - Valencia, Spain Duration: 26 Jun 2024 → 28 Jun 2024 Conference number: 6 https://carmaconf.org/program/ |
Conference
| Conference | 6th International Conference on Advanced Research Methods and Analytics, CARMA 2024 |
|---|---|
| Abbreviated title | CARMA 2024 |
| Country/Territory | Spain |
| City | Valencia |
| Period | 26/06/24 → 28/06/24 |
| Internet address |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
Keywords
- Forecasting
- time series
- rolling window
- expanding window
- Unemployment
- google trends
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