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Stochastic Gradient Variational Bayes and Normalizing Flows for Estimating Macroeconomic Models

Author

Listed:
  • Ramis Khbaibullin

    (Bank of Russia, Russian Federation)

  • Sergei Seleznev

    (Bank of Russia, Russian Federation)

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.

Suggested Citation

  • Ramis Khbaibullin & Sergei Seleznev, 2020. "Stochastic Gradient Variational Bayes and Normalizing Flows for Estimating Macroeconomic Models," Bank of Russia Working Paper Series wps61, Bank of Russia.
    • Handle: RePEc:bkr:wpaper:wps61
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    Citations

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    Cited by:

    1. Sergey Seleznev & Natalia Turdyeva & Ramis Khabibullin & Anna Tsvetkova, 2020. "Seasonal adjustment of the Bank of Russia Payment System financial flows data," Bank of Russia Working Paper Series wps65, Bank of Russia.
    2. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    3. Natalia Turdyeva & Anna Tsvetkova & Levon Movsesyan & Alexey Porshakov & Dmitriy Chernyadyev, 2021. "Data of Sectoral Financial Flows as a High-Frequency Indicator of Economic Activity," Russian Journal of Money and Finance, Bank of Russia, vol. 80(2), pages 28-49, June.
    4. Ramis Khabibullin & Alexey Ponomarenko, 2022. "An empirical behavioral model of household’s deposit dollarization," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(3), pages 827-847, July.

    More about this item

    Keywords

    Stochastic gradient variational Bayes; normalizing flows; mean-field approximation; sparse Bayesian learning; BVAR; Bayesian neural network; DFM.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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