This paper introduces a reinforcement learning based approach to compute optimal interest rate reaction functions in terms of fulfilling inflation and output gap targets. The method is generally flexible enough to incorporate restrictions like the zero lower bound, nonlinear economy structures...

This paper analyses the forecasting performance of monetary policy reaction functions using U.S. Federal Reserve's Greenbook real-time data. The results indicate that artificial neural networks are able to predict the nominal interest rate better than linear and nonlinearTaylor rule models as...

This paper analyzes the main uncertainty of college saving - the child’s ability - in the context of the saving with learning model. The first section develops a dynamic model combining asset accumulation and learning to explain the parents’ forward-looking saving behavior when they are...

This paper considers the problem of changing prices over time to maximize expected revenues in the presence of unknown demand distribution parameters. It provides and compares several methods that use the sequence of past prices and observed demands to set price in the current period. A Taylor...

How can naivete about present bias persist despite experience? To answer this question, our experiment investigates participants' ability to learn from their own behavior. Participants decide how much to work on a real effort task on two predetermined dates. In the week preceding each work date,...

Virtually each seasonal adjustment software includes an ensemble of seasonality tests for assessing whether a given time series is in fact a candidate for seasonal adjustment. However, such tests are certain to produce either the same resultor conflicting results, raising the question if there...