DP15346 Deep Learning Classification: Modeling Discrete Labor Choice

Author(s): Lilia Maliar, Serguei Maliar
Publication Date: October 2020
Keyword(s): classification, deep learning, discrete choice, Indivisible labor, intensive and extensive margins, logistic regression, neural network
Programme Areas: Monetary Economics and Fluctuations
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=15346

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.