Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246693 
Authors: 
Year of Publication: 
2021
Series/Report no.: 
School of Economics Discussion Papers No. 2108
Publisher: 
University of Kent, School of Economics, Canterbury
Abstract: 
This paper provides a reverse mode derivative for DSGE models. Reverse mode differentiation enables the efficient computation of gradients from the model likelihood to the model parameters. These gradients can then be used by derivative based sampling algorithms including the No U-Turn Sampler. Benchmarks are provided using a small scale New Keynesian model. Our benchmarks demonstrate that MCMC chains generated using the No U-turn Sampler converge much more quickly than those generated using Metropolis Hastings.
Subjects: 
DSGE
Reverse mode differentiation
Hamiltonian Monte Carlo
No U-Turn Sampler
Bayesian estimation
JEL: 
C11
C13
C32
Document Type: 
Working Paper

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