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 …

determining the payoffs ‘judgment.' There is a risky action, whose payoff depends on the state, and a safe action with the same … payoff in every state. Judgment is costly; for each potential state, it requires thought on what the payoff might be …. Prediction and judgment are complements as long as judgment is not too difficult. We show that in complex environments with a …