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<p>Estimating DSGE Models using Multilevel Sequential Monte Carlo in Approximate Bayesian Computation</p>

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Title Statement <p>Estimating DSGE Models using Multilevel Sequential Monte Carlo in Approximate Bayesian Computation</p>
 
Added Entry - Uncontrolled Name Alaminos, David ; Department of Mechanical Engineering and Energy Efficiency, University of Málaga, Campus El Ejido s/n, 29071 Málaga, Spain
Ramírez, Ana ; Department of Mechanical Engineering and Energy Efficiency, University of Málaga, Campus El Ejido s/n, 29071 Málaga, Spain
Fernández-Gámez, Manuel A; Department of Finance and Accounting, University of Málaga, Campus El Ejido s/n, 29071 Málaga, Spain
Becerra-Vicario, Rafael ; Department of Finance and Accounting, University of Málaga, Campus El Ejido s/n, 29071 Málaga, Spain
 
Uncontrolled Index Term Approximate bayesian computation; Dynamic general equilibrium models; Macroeconomic forecasting; Monte Carlo algorithms
 
Summary, etc. 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.
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2022-11-09 07:52:48
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/67988
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 79, ##issue.no## 1 (2020): Journal of Scientific & Industrial Research
 
Language Note en