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Estimating Non-Linear DSGEs with the Approximate Bayesian Computation: an application to the Zero Lower Bound

Valerio Scalone ()
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Valerio Scalone: Dipartimento di Scienze Sociali ed Economiche, Sapienza University of Rome (Italy).

No 6/15, Working Papers from Sapienza University of Rome, DISS

Abstract: Non-linear model estimation is generally perceived as impractical and computationally burdensome. This perception limited the diffusion on non-linear models estimation. In this paper a simple set of techniques going under the name of Approximate Bayesian Computation (ABC) is proposed. ABC is a set of Bayesian techniques based on moments matching: moments are obtained simulating the model conditional on draws from the prior distribution. An accept-reject criterion is applied on the simulations and an approximate posterior distribution is obtained by the accepted draws. A series of techniques are presented (ABC-regression, ABC-MCMC, ABC-SMC). To assess their small sample performance, Montecarlo experiments are run on AR(1) processes and on a RBC model showing that ABC estimators outperform the Limited Information Method (Kim, 2002), a GMM-style estimator. In the remainder, the estimation of a new-keynesian model with a zero lower bound on the interest rate is performed. Non-gaussian moments are exploited in the estimation procedure.

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-ecm, nep-mac and nep-ore
Date: 2015-11
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