Data Cloning Estimation and Identification of a Medium-Scale DSGE Model

We apply the data cloning method to estimate a medium-scale dynamic stochastic general equilibrium model. The data cloning algorithm is a numerical method that employs replicas of the original sample to approximate the maximum likelihood estimator as the limit of Bayesian simulation-based estimators...

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Main Authors: Pedro Chaim, Márcio Poletti Laurini
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Stats
Subjects:
Online Access:https://www.mdpi.com/2571-905X/6/1/2
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author Pedro Chaim
Márcio Poletti Laurini
author_facet Pedro Chaim
Márcio Poletti Laurini
author_sort Pedro Chaim
collection DOAJ
description We apply the data cloning method to estimate a medium-scale dynamic stochastic general equilibrium model. The data cloning algorithm is a numerical method that employs replicas of the original sample to approximate the maximum likelihood estimator as the limit of Bayesian simulation-based estimators. We also analyze the identification properties of the model. We measure the individual identification strength of each parameter by observing the posterior volatility of data cloning estimates and access the identification problem globally through the maximum eigenvalue of the posterior data cloning covariance matrix. Our results corroborate existing evidence suggesting that the DSGE model of Smeets and Wouters is only poorly identified. The model displays weak global identification properties, and many of its parameters seem locally ill-identified.
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spelling doaj.art-90f9f7307e3241e5b8e7c333aab632402023-11-17T13:53:45ZengMDPI AGStats2571-905X2022-12-0161172910.3390/stats6010002Data Cloning Estimation and Identification of a Medium-Scale DSGE ModelPedro Chaim0Márcio Poletti Laurini1Departament of Economics, Federal University of Santa Catarina, Florianópolis 88040-900, SC, BrazilDepartment of Economics, School of Economics, Business Administration and Accounting at Ribeirão Preto (FEA-RP/USP), Av. dos Bandeirantes 3900, FEARP—University of São Paulo, Ribeirão Preto 14040-905, SP, BrazilWe apply the data cloning method to estimate a medium-scale dynamic stochastic general equilibrium model. The data cloning algorithm is a numerical method that employs replicas of the original sample to approximate the maximum likelihood estimator as the limit of Bayesian simulation-based estimators. We also analyze the identification properties of the model. We measure the individual identification strength of each parameter by observing the posterior volatility of data cloning estimates and access the identification problem globally through the maximum eigenvalue of the posterior data cloning covariance matrix. Our results corroborate existing evidence suggesting that the DSGE model of Smeets and Wouters is only poorly identified. The model displays weak global identification properties, and many of its parameters seem locally ill-identified.https://www.mdpi.com/2571-905X/6/1/2data cloningDSGEidentificationMCMC
spellingShingle Pedro Chaim
Márcio Poletti Laurini
Data Cloning Estimation and Identification of a Medium-Scale DSGE Model
Stats
data cloning
DSGE
identification
MCMC
title Data Cloning Estimation and Identification of a Medium-Scale DSGE Model
title_full Data Cloning Estimation and Identification of a Medium-Scale DSGE Model
title_fullStr Data Cloning Estimation and Identification of a Medium-Scale DSGE Model
title_full_unstemmed Data Cloning Estimation and Identification of a Medium-Scale DSGE Model
title_short Data Cloning Estimation and Identification of a Medium-Scale DSGE Model
title_sort data cloning estimation and identification of a medium scale dsge model
topic data cloning
DSGE
identification
MCMC
url https://www.mdpi.com/2571-905X/6/1/2
work_keys_str_mv AT pedrochaim datacloningestimationandidentificationofamediumscaledsgemodel
AT marciopolettilaurini datacloningestimationandidentificationofamediumscaledsgemodel