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Exploring new procedures to deal with time-varying relationships in forecasting: A study in the context of forecasting youth unemployment with Google searches.

Bruijn, N.B. de (2023) Exploring new procedures to deal with time-varying relationships in forecasting: A study in the context of forecasting youth unemployment with Google searches.

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Abstract:Forecasting has evolved dramatically over time, from humans looking at the sky to predict weather conditions to an era characterized by an increasing imperative to forecast, a surge in data, and advanced computational capabilities. Despite these developments, forecasters continue to rely on outdated forecasting procedures. In this study, a crucial limitation is addressed: the assumption that relationships between phenomena are constant. To address this issue, traditional procedures are tested against innovated procedures. Results show that the innovated procedures, tailored to the use of big data in forecasting and leveraging increased computational capabilities, are better able to capture time-varying relationships between variables. As a result, forecasts of the youth unemployment rate are up to 44% more accurate. With the innovated procedures, practitioners are assisted in building big data capability, resulting in improved forecasts and better decision-making. Additionally, this study makes labour market forecasting an accessible endeavour for all organizations by sharing the algorithm to accurately forecast the youth unemployment rate with publicly available data. Finally, this study furnishes forecasting literature with a novel perspective, stressing a reconsideration of methodologies before exploiting more complex models in a search for greater forecasting accuracy.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:83 economics, 85 business administration, organizational science
Programme:Business Administration MSc (60644)
Link to this item:https://purl.utwente.nl/essays/97250
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