Estimating Non-Linear DSGEs with the Approximate Bayesian Computation: an application to the Zero Lower Bound
Working papers from Banque de France
Estimation of non-linear DSGE models is still very limited due to high computational costs and identification issues arising from the non-linear solution of the models. Besides, the use of small sample amplifies those issues. This paper advocates for the use of Approximate Bayesian Computation (ABC), a set of Bayesian techniques based on moments matching. First, through Monte Carlo exercises, I assess the small sample performance of ABC estimators and run a comparison with the Limited Information Method (Kim, 2002), the state-of-the-art Bayesian method of moments used in DSGE literature. I find that ABC has a better small sample performance, due to the more efficient way through which the information provided by the moments is used to update the prior distribution. Second, ABC is tested on the estimation of a new-Keynesian model with a zero lower bound, a real life application where the occasionally binding constraint complicates the use of traditional method of moments.
Keywords: Monte Carlo analysis; Method of moments, Bayesian, Zero Lower Bound, DSGE estimation (search for similar items in EconPapers)
JEL-codes: C15 C11 E2 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-ets and nep-mac
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