Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/319986 
Year of Publication: 
2015
Series/Report no.: 
CASMEF Working Paper Series No. WP 2015/09
Publisher: 
LUISS Guido Carli, Department of Economics and Finance, Arcelli Centre for Monetary and Financial Studies, Rome
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.
Document Type: 
Working Paper

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