Deep learning classification: Modeling discrete labor choice

Abstract

We introduce a deep learning classification (DLC) method for analyzing equilibrium in discrete-continuos choice dynamic models. As an illustration, we solve 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 state-contingent discontinuous decision functions that tell us when the agent switches from one employment state to another. We use deep learning not only to characterize the discrete indivisible labor 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.