| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15346 |
| DP15346 Deep Learning Classification: Modeling Discrete Labor Choice | |
| Serguei Maliar | |
| 发表日期 | 2020-10-07 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Monetary Economics and Fluctuations |
| 关键词 | Deep learning Neural network Logistic regression Classification Discrete choice Indivisible labor Intensive and extensive margins |
| URL | https://cepr.org/publications/dp15346 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/544328 |
| 推荐引用方式 GB/T 7714 | Serguei Maliar. DP15346 Deep Learning Classification: Modeling Discrete Labor Choice. 2020. |
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