Stochastic Gradient Variational Bayes and Normalizing Flows for Estimating Macroeconomic Models

Ramis Khbaibullin () and Sergei Seleznev
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Ramis Khbaibullin: Bank of Russia, Russian Federation

No wps61, Bank of Russia Working Paper Series from Bank of Russia

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.

Keywords: Stochastic gradient variational Bayes; normalizing flows; mean-field approximation; sparse Bayesian learning; BVAR; Bayesian neural network; DFM. (search for similar items in EconPapers)
JEL-codes: C11 C32 C45 E17 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2020-10
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets, nep-mac and nep-ore
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Citations: View citations in EconPapers (4)

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