Deep Learning Classification: Modeling Discrete Labor Choice

Serguei Maliar

No 15346, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: 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.

Keywords: Deep learning; Neural network; Logistic regression; Classification; Discrete choice; Indivisible labor; Intensive and extensive margins (search for similar items in EconPapers)
Date: 2020-10
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dcm and nep-dge
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Citations: View citations in EconPapers (1)

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Journal Article: Deep learning classification: Modeling discrete labor choice (2022)
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