Estimating DSGE Models using Multilevel Sequential Monte Carlo in Approximate Bayesian Computation

被引：0

作者：

Alaminos, David ^{[1
]}

Ramirez, Ana ^{[1
]}

Fernandez-Gamez, Manuel A. ^{[2
]}

Becerra-Vicario, Rafael ^{[2
]}

机构：

[1] Univ Malaga, Dept Mech Engn & Energy Efficiency, Campus El Ejido S-N, E-29071 Malaga, Spain

[2] Univ Malaga, Dept Finance & Accounting, Campus El Ejido S-N, E-29071 Malaga, Spain

来源：

JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH | 2020年 / 79卷 / 01期

关键词：

Dynamic General Equilibrium Models; Monte Carlo algorithms; Approximate Bayesian Computation; Macroeconomic forecasting;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：21 / 25

页数：5

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