Essays on measuring uncertainty

Abstract

This dissertation consists of six papers that focus on the measurement of uncertainty in economic contexts and offer different methods for quantifying uncertainty. The main objective is to contribute to the body of research on uncertainty in economics. The work largely uses text processing techniques, with the premise that uncertainty is a latent variable that cannot be measured directly, but can be approximated through communication. The first paper measures uncertainty in terms of future forecast errors, which are useful to economic decision makers and show the ability to predict future growth of industrial production. The research then moves to text-based measures of uncertainty, with two papers examining how online users' uncertainty can predict future economic developments. The final three papers examine central bank communication and find that uncertainty sentiment fluctuates over time but remains consistent across speakers. An uncertainty index derived from central bank speeches is shown to be an effective predictor of economic outcomes. The final chapter empirically tests the global central bank uncertainty index and confirms its predictive power for economic developments, at times outperforming previous indicators. Ultimately, this dissertation provides valuable tools for measuring and forecasting economic uncertainty, primarily through text-based indicators.