infinite-horizon learning, and subjective expectations with Euler-equation learning. Under rational expectations, the model … necessitates of large, possibly unrealistically so, degrees of myopia. The same result persists under infinite-horizon learning …, given that agents are still remarkably far-sighted. But, under Euler-equation learning, the model can fit the data with only …

We develop a dynamic learning model to study money adoption. Agents own an asset that can potentially serve as money … distribution of dividends and based on the learning outcomes they coordinate to adopt the asset as money or to abandon it. We …

This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a … basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing …

In state-space models, parameter learning is practically difficult and is still an open issue. This paper proposes an … efficient simulation-based parameter learning method. First, the approach breaks up the interdependence of the hidden states and …