Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/311805 
Year of Publication: 
2025
Series/Report no.: 
Bank of Canada Staff Working Paper No. 2025-3
Publisher: 
Bank of Canada, Ottawa
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.
Subjects: 
Business fluctuations and cycles
Economic models
JEL: 
C61
C63
E37
E47
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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