Artificial neural networks to solve dynamic programming problems: a bias-corrected Monte Carlo operator
From MaRDI portal
Publication:6572634
DOI10.1016/J.JEDC.2024.104853MaRDI QIDQ6572634
Publication date: 16 July 2024
Published in: Journal of Economic Dynamics and Control (Search for Journal in Brave)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Computing equilibrium in OLG models with stochastic production
- A generalization of the endogenous grid method
- Asymptotic methods for aggregate growth models
- A penalized Fischer-Burmeister NCP-function
- Multilayer feedforward networks are universal approximators
- Computing the variance of a conditional expectation via non-nested Monte Carlo
- Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain
- Deep learning classification: modeling discrete labor choice
- Reducing the Dimensionality of Data with Neural Networks
- Estimatlon Of μ2 in Normal Population
- Universal approximation bounds for superpositions of a sigmoidal function
- Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models
- Linear Models in Statistics
- Idiosyncratic Shocks and the Role of Nonconvexities in Plant and Aggregate Investment Dynamics
- DEEP EQUILIBRIUM NETS
- Financial frictions and the wealth distribution
This page was built for publication: Artificial neural networks to solve dynamic programming problems: a bias-corrected Monte Carlo operator
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6572634)