| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP17391 |
| DP17391 Estimating Nonlinear Heterogeneous Agents Models with Neural Networks | |
| Leonardo Melosi; Matthias Rottner | |
| 发表日期 | 2022-06-17 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While relaxing these assumptions is well-known to give rise to complicated curse-of-dimensionality problems, it is often unclear how seriously these simplifications distort the dynamics and predictions of the model. We leverage the recent advancements in machine learning to develop a solution and estimation method based on neural networks that does not require these strong assumptions. We apply our method to a nonlinear Heterogeneous Agents New Keynesian (HANK) model with a zero lower bound (ZLB) constraint for the nominal interest rate to show that the method is much more efficient than existing global solution methods and that the estimation converges to the true parameter values. Further, this application sheds light on how effectively our method is capable to simultaneously deal with a large number of state variables and parameters, nonlinear dynamics, heterogeneity as well as aggregate uncertainty. |
| 主题 | Macroeconomics and Growth ; Monetary Economics and Fluctuations |
| 关键词 | Machine learning Neural networks Bayesian estimation Global solution Heterogeneous agents Nonlinearities Aggregate uncertainty Hank model Zero lower bound |
| URL | https://cepr.org/publications/dp17391-0 |
| 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/546452 |
| 推荐引用方式 GB/T 7714 | Leonardo Melosi,Matthias Rottner. DP17391 Estimating Nonlinear Heterogeneous Agents Models with Neural Networks. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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