This paper introduces a machine learning algorithm called Sequential Neural Posterior Estimation (SNPE) for estimating structural heterogeneous agent macroeconomic models. SNPE is a simulation-based algorithm that aims to approximate the joint distribution of parameters and simulated data. It then uses a conditional density estimator to obtain the posterior distribution. The algorithm offers several advantages, including the ability to handle any black box solution method, improved speed and accuracy compared to existing Bayesian algorithms, and statistically efficient simulation-based estimates. The algorithm outperforms Markov Chain Monte Carlo in estimating Bayesian posteriors for benchmark models such as Smets-Wouters (2007) and improves estimation speed for Heterogeneous Agent New Keynesian (HANK) models. An empirical application demonstrates the effectiveness of this technique in estimating a heterogeneous agent model using both aggregate time series and cross-sectional micro-data.
A tutorial can be found at: https://shorturl.at/pMQ18
This paper uses a transformer neural network model to perform imputation of missing data. The method returns a distribution which allows for easy marginalization which can allow for statistically efficient analysis when combined with a model for inference.
This paper demonstrates how to use Variational Inference as well as a flexible variational family that allows fast estimation of DSGE models, which roughly reduces computation time by a factor of 100.
This paper demonstrates how to use variational inference as well as a flexible variational family that allows fast estimation of DSGE models, which roughly reduces computation time by a factor of 100 compared to Markov chain Monte Carlo.