Template-Type: ReDIF-Paper 1.0
Author-Name: Ramis Khbaibullin
Author-Email: KhabibullinRA@cbr.ru
Author-Workplace-Name: Bank of Russia, Russian Federation

Author-Name: Sergei Seleznev
Author-Email: SeleznevSM@cbr.ru
Author-Workplace-Name: Bank of Russia, Russian Federation




Title: Stochastic Gradient Variational Bayes and Normalizing Flows for Estimating Macroeconomic Models

Abstract: We illustrate the ability of the stochastic gradient variational Bayes algorithm, which is a
very popular machine learning tool, to work with macrodata and macromodels. Choosing two
approximations (mean-field and normalizing flows), we test properties of algorithms for a set of
models and show that these models can be estimated fast despite the presence of estimated
hyperparameters. Finally, we discuss the difficulties and possible directions of further research.


Length: 49 pages
Creation-Date: 2020-10
Revision-Date:
Publication-Status:
File-URL: http://cbr.ru/Content/Document/File/112571/wp-61_e.pdf
File-Format: Application/pdf
File-Function:
Number:wps61
Classification-JEL:  C11, C32, C32, C45, E17. 
Keywords: Stochastic gradient variational Bayes, normalizing flows, mean-field approximation, sparse Bayesian learning, BVAR, Bayesian neural network, DFM. 
Handle:RePEc:bkr:wpaper:wps61