We empirically investigate how well different learning rules manage to explain the formation of household inflation expectations in six key member countries of the euro area. Our findings reveal a pronounced heterogeneity in the learning rules employed on the country level. While the expectation...

We applied an ensemble learning method known as random forests, which is widely used in supervised machine learning, to predict the profitability ratio of dairy farms based on financial and production related variables. The predictive model was implemented as a web service to enable farmers to...

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of...