Estimation of DSGE models with the effective lower bound

Gregor Boehl and Felix Strobel

Journal of Economic Dynamics and Control, 2024, vol. 158, issue C

Abstract: We propose a new approach for the efficient and robust Bayesian estimation of medium- and large-scale DSGE models with occasionally binding constraints. At its core lies the Ensemble Kalman filter, a novel nonlinear recursive filter, which allows for fast likelihood approximations even for models with large state spaces. We combine the filter with a computationally efficient solution method for piece-wise linear models a state-of-the-art MCMC sampler. Using artificial data, we demonstrate that our approach accurately captures the true parameters of models with a lower bound on nominal interest rates, even with very long lower bound episodes. We use the approach to analyze the US business cycle dynamics until the Covid-19 pandemic, with a focus on the long lower bound episode after the Global Financial Crisis.

Keywords: Effective lower bound; Bayesian estimation; Great recession; Nonlinear likelihood inference; Ensemble Kalman filter (search for similar items in EconPapers)
JEL-codes: C11 C63 E31 E32 E44 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Working Paper: Estimation of DSGE Models With the Effective Lower Bound (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:158:y:2024:i:c:s0165188923001902

DOI: 10.1016/j.jedc.2023.104784

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