Deep learning classification: Modeling discrete labor choice

Lilia Maliar and Serguei Maliar

Journal of Economic Dynamics and Control, 2022, vol. 135, issue C

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.

Keywords: Artificial intelligence; Machine learning; Deep learning; Neural network; Logistic regression; Softmax regression; Classification; Discrete choice; Indivisible labor (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Working Paper: Deep Learning Classification: Modeling Discrete Labor Choice (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:135:y:2022:i:c:s016518892100230x

DOI: 10.1016/j.jedc.2021.104295

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