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Differentiable, Filter Free Bayesian Estimation of DSGE Models Using Mixture Density Networks

Author

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  • Chris Naubert

Abstract

I develop a methodology for Bayesian estimation of globally solved, non-linear macroeconomic models. A novel feature of my method is the use of a mixture density network to approximate the distribution of initial states. I use the methodology to estimate a medium-scale, two-agent New Keynesian model with irreversible investment and a zero lower bound on nominal interest rates. Using simulated data, I show that the method is able to recover the “true” parameters when using the mixture density network approximation of the initial state distribution. This contrasts with the case when the initial states are set to their steady-state values.

Suggested Citation

  • Chris Naubert, 2025. "Differentiable, Filter Free Bayesian Estimation of DSGE Models Using Mixture Density Networks," Staff Working Papers 25-3, Bank of Canada.
    • Handle: RePEc:bca:bocawp:25-3
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    More about this item

    Keywords

    Business Fluctuations and Cycles; Economic Models;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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