NON-LINEAR DSGE MODELS AND THE CENTRAL DIFFERENCE KALMAN FILTER

Authors


  • This paper is an improved version of some sections in an earlier paper entitled: ‘Non-linear DSGE Models, the Central Difference Kalman Filter, and the Mean Shifted Particle Filter’.

Correspondence to: Martin M. Andreasen, Bank of England, Threadneedle Street, London EC2R 8AH, UK. E-mail: mmandreasendk@gmail.com

SUMMARY

This paper introduces a quasi maximum likelihood approach based on the central difference Kalman filter to estimate non-linear dynamic stochastic general equilibrium (DSGE) models with potentially non-Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models solved up to third order. These properties are verified in a Monte Carlo study for a DSGE model solved to second and third order with structural shocks that are Gaussian, Laplace distributed, or display stochastic volatility. Copyright © 2012 John Wiley & Sons, Ltd.

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