Google Trends Forecasting: Model re-specification of time-series for enhancing forecasts of youth unemployment

Abstract

Abstract In the last decades, the forecasting field has been using the surge in big data and advanced computational capabilities. Despite these developments, forecasters continue using traditional forecasting procedures that assume static relationships between phenomena. To address the reality of dynamic relations among phenomena, this study discusses time-variant re-specification methods as part of time-series based forecasts and compares the outcomes with the traditional procedures. This method-comparison is applied to a real-world exercise, the forecasting of Dutch youth unemployment with big data based on Google Trends. For youth unemployment forecasts, our results show 44% more forecasting accuracy by time-varying forecasting models than the traditional static forecasting models. Additionally, this study makes labour market forecasting an accessible endeavour to all organizations by sharing the algorithm for forecasting youth unemployment rates with publicly available data such as Google Trends. Moreover, our study stresses a reconsideration of forecasting methodologies towards model re-specification instead of model recalibration.