DP15346 Deep Learning Classification: Modeling Discrete Labor Choice
We introduce a deep learning classification (DLC) method for analyzing equilibrium in discrete-continuous
choice dynamic models. As an illustration, we apply the DLC method to solve a version of
Krusell and Smith's (1998) heterogeneous-agent model with incomplete markets, borrowing constraint
and indivisible labor choice. The novel feature of our analysis is that we construct discontinuous decision
functions that tell us when the agent switches from one employment state to another, conditional on the
economy's state. We use deep learning not only to characterize the discrete indivisible choice but also
to perform model reduction and to deal with multicollinearity. Our TensorFlow-based implementation
of DLC is tractable in models with thousands of state variables.
