engleski

Economic Policy Uncertainty Index Meets Ensemble Learning

We utilize a battery of ensemble learning techniques [ensemble linear regression (LM), random forest], as well as two gradient boosting techniques [Gradient Boosting Decision Tree and Extreme Gradient Boosting (XGBoost)] to scrutinize the possibilities of enhancing the predictive accuracy of Economic Policy Uncertainty (EPU) index. Applied to a data-rich environment of the Newsbank media database, our LM and XGBoost assessments mostly outperform the other two ensemble learning procedures, as well as the original EPU index. Our LM and XGBoost estimates bring EPU closer to the stylized facts of uncertainty than other uncertainty estimates. LM and XGBoost indicators are more countercyclical and have more pronounced leading properties. We find that EPU is more strongly correlated to financial volatility measures than to consumers’ assessments of uncertainty. This corroborates that the media place a much higher weight on the financial sector than on the economic issues of consumers. Further on, we considerably widen the scope of search terms included in the calculation of EPU index. Using ensemble learning techniques on such a rich set of keywords, we mostly manage to outperform the standard EPU in terms of correlation with standard uncertainty proxies. We also find that the predictive accuracy of EPU index can be considerably increased using a more diversified set of uncertainty-related terms than the original EPU framework. Our estimates perform much better in a monthly setting (targeting the industrial production growth) than targeting quarterly GDP growth. This speaks in favor of uncertainty as a purely short-term phenomenon.

Economic policy uncertainty index ; Textual analysis ; Ensemble learning ; Random forest model ; Gradient boosting

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