Estimation of agent-based models using Bayesian deep learning approach of BayesFlow

DOI10.1016/j.jedc.2021.104082zbMath1475.91160OpenAlexW3128827037MaRDI QIDQ2246641

Takashi Shiono

Publication date: 16 November 2021

Published in: Journal of Economic Dynamics \& Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jedc.2021.104082

zbMATH Keywords

parameter estimationneural networkBayesian inferenceagent-based modeldeep learning

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Artificial neural networks and deep learning (68T07) Bayesian inference (62F15) Principal-agent models (91B43)

Uses Software

• LSTM
• BayesFlow

Cites Work

• Estimation of dynamic models with nonparametric simulated maximum likelihood
• Emergent dynamics of a macroeconomic agent based model with capital and credit
• Estimation of ergodic agent-based models by simulated minimum distance
• Bayesian estimation of agent-based models
• Agent based-stock flow consistent macroeconomics: towards a benchmark model
• Estimation of financial agent-based models with simulated maximum likelihood
• Agent-based model calibration using machine learning surrogates
• Estimation of agent-based models using sequential Monte Carlo methods
• A comparison of economic agent-based model calibration methods
• Neural network with unbounded activation functions is universal approximator
• Algebraic Geometry and Statistical Learning Theory
• Universal approximation bounds for superpositions of a sigmoidal function
• Approximation by superpositions of a sigmoidal function

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