Recently, the literature has measured economic policy uncertainty using news references, resulting in the frequently-mentioned 'Economic Policy Uncertainty index' (EPU). In the original setup, a news article is assumed to address policy uncertainty if it contains certain predefined keywords. We argue that the original setup is prone to measurement error, and propose an alternative methodology using text mining techniques. We compare the original method to modality annotation and support vector machines (SVM) classification in order to create an EPU index for Belgium. Validation on an out-of-sample test set speaks in favour of using an SVM classification model for constructing a news-based policy uncertainty indicator. The indicators are then used to forecast 10 macroeconomic and financial variables. The original method of measuring EPU does not have predictive power for any of these 10 variables. The SVM indicator has a higher predictive power and, notably, changes in the level of policy uncertainty during tumultuous periods of high uncertainty and risk can predict changes in the sovereign bond yield and spread, the credit default swap spread, and consumer confidence.

Additional Metadata
Keywords Economic policy, Forecasting, Text mining, Uncertainty
Persistent URL dx.doi.org/10.1016/j.ijforecast.2016.08.006, hdl.handle.net/1765/94796
Journal International Journal of Forecasting
Citation
Tobback, E. (Ellen), Naudts, H. (Hans), Daelemans, W. (Walter), Junqué de Fortuny, E, & Martens, D. (David). (2016). Belgian economic policy uncertainty index: Improvement through text mining. International Journal of Forecasting. doi:10.1016/j.ijforecast.2016.08.006