Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/53469
Title: Estimating DSGE Models using Multilevel Sequential Monte Carlo in Approximate Bayesian Computation
Authors: Alaminos, David
Ramírez, Ana
Fernández-Gámez, Manuel A
Becerra-Vicario, Rafael
Keywords: Dynamic General Equilibrium Models;Monte Carlo algorithms;Approximate Bayesian Computation;Macroeconomic forecasting
Issue Date: Jan-2020
Publisher: NISCAIR-CSIR, India
Abstract: Dynamic Stochastic General Equilibrium (DSGE) models allow for probabilistic estimations with the aim of formulating macroeconomic policies and monitoring them. In this study, we propose to apply the Sequential Monte Carlo Multilevel algorithm and Approximate Bayesian Computation (MLSMC-ABC) to increase the robustness of DSGE models built for small samples and with irregular data. Our results indicate that MLSMC-ABC improves the estimation of these models in two aspects. Firstly, the accuracy levels of the existing models are increased, and secondly, the cost of the resources used is reduced due to the need for shorter execution time.
Page(s): 21-25
URI: http://nopr.niscair.res.in/handle/123456789/53469
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.79(01) [January 2020]

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