What drives the estimation results of DSGE models? Effect of
the input data on parameter estimates

D 2018

What drives the estimation results of DSGE models? Effect of the input data on parameter estimates

CHALMOVIANSKÝ, Jakub

Basic information

Original name

What drives the estimation results of DSGE models? Effect of the input data on parameter estimates

Authors

CHALMOVIANSKÝ, Jakub (703 Slovakia, guarantor, belonging to the institution)

Edition

Prague, Proceedings of 36th International Conference Mathematical Methods in Economics, p. 169-174, 6 pp. 2018

Publisher

MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

50202 Applied Economics, Econometrics

Country of publisher

Czech Republic

Confidentiality degree

is not subject to a state or trade secret

Publication form

storage medium (CD, DVD, flash disk)

References:

Conference proceedings

RIV identification code

RIV/00216224:14560/18:00103666

Organization unit

Faculty of Economics and Administration

ISBN

978-80-7378-372-3

UT WoS

000507455300030

Keywords in English

Bayesian estimation; DSGE model; Matching moments; Model simulation; Parameter identification

Abstract

V originále

In this contribution, I compare three different Bayesian dynamic stochastic general equilibrium (DSGE) models in a simulation-estimation exercise. This exercise is aimed at revealing the capabilities of these models to re-estimate, during the estimation phase, values of parameters previously set in the simulation phase. The first model is the renowned work of Smets and Wouters. The second one is the rather small DSGE model of a closed economy with search and matching frictions on labour market proposed by Lubik. The third one is based on the paper written by Sheen and Wang, where they introduce a model of a small open economy with various labour market frictions. The aim of this contribution is to examine how the complexity of the model and the amount of information needed, represented by the number of observations in the observables, affect the results when the parameters are estimated. At first, I shortly introduce all presented models. Based on the given calibration, trajectories of main endogenous variables are simulated. These simulated trajectories with a various number of observations are then used as observables for estimation of the model parameters to reveal how rich information is needed for each model to properly identify its parameters.

Links

MUNI/A/0966/2017, interní kód MU

Name: Nekonvenční monetární politika a instituce trhu práce pohledem dynamických stochastických modelů všeobecné rovnováhy (Acronym: Nekonvenční monetární politika)

Investor: Masaryk University, Category A

Cite

CHALMOVIANSKÝ, Jakub. What drives the estimation results of DSGE models? Effect of the input data on parameter estimates. In Lucie Váchová, Václav Kratochvíl. Proceedings of 36th International Conference Mathematical Methods in Economics. Prague: MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University, 2018, p. 169-174. ISBN 978-80-7378-372-3.

@inproceedings{1435578,
   author = {Chalmovianský, Jakub},
   address = {Prague},
   booktitle = {Proceedings of 36th International Conference Mathematical Methods in Economics},
   editor = {Lucie Váchová, Václav Kratochvíl},
   keywords = {Bayesian estimation; DSGE model; Matching moments; Model simulation; Parameter identification},
   howpublished = {paměťový nosič},
   language = {eng},
   location = {Prague},
   isbn = {978-80-7378-372-3},
   pages = {169-174},
   publisher = {MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University},
   title = {What drives the estimation results of DSGE models? Effect of the input data on parameter estimates},
   url = {https://mme2018.fm.vse.cz/},
   year = {2018}
}

TY  - CONF
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AU  - Chalmovianský, Jakub
PY  - 2018
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PB  - MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University
CY  - Prague
SN  - 9788073783723
KW  - Bayesian estimation
KW  - DSGE model
KW  - Matching moments
KW  - Model simulation
KW  - Parameter identification
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L2  - https://mme2018.fm.vse.cz/
N2  - In this contribution, I compare three different Bayesian dynamic stochastic general equilibrium (DSGE) models in a simulation-estimation exercise. This exercise is aimed at revealing the capabilities of these models to re-estimate, during the estimation phase, values of parameters previously set in the simulation phase. The first model is the renowned work of Smets and Wouters. The second one is the rather small DSGE model of a closed economy with search and matching frictions on labour market proposed by Lubik. The third one is based on the paper written by Sheen and Wang, where they introduce a model of a small open economy with various labour market frictions. The aim of this contribution is to examine how the complexity of the model and the amount of information needed, represented by the number of observations in the observables, affect the results when the parameters are estimated. At first, I shortly introduce all presented models. Based on the given calibration, trajectories of main endogenous variables are simulated. These simulated trajectories with a various number of observations are then used as observables for estimation of the model parameters to reveal how rich information is needed for each model to properly identify its parameters.
ER  -

CHALMOVIANSKÝ, Jakub. What drives the estimation results of DSGE models? Effect of the input data on parameter estimates. In Lucie Váchová, Václav Kratochvíl. \textit{Proceedings of 36th International Conference Mathematical Methods in Economics}. Prague: MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University, 2018, p.~169-174. ISBN~978-80-7378-372-3.

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