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

Základní údaje

Originální název

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

Autoři

CHALMOVIANSKÝ, Jakub (703 Slovensko, garant, domácí)

Vydání

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

Nakladatel

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

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

50202 Applied Economics, Econometrics

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

paměťový nosič (CD, DVD, flash disk)

Odkazy

Conference proceedings

Kód RIV

RIV/00216224:14560/18:00103666

Organizační jednotka

Ekonomicko-správní fakulta

ISBN

978-80-7378-372-3

UT WoS

000507455300030

Klíčová slova anglicky

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

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 27. 4. 2020 21:37, Mgr. Daniela Marcollová

Anotace

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.

Návaznosti

MUNI/A/0966/2017, interní kód MU

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

Investor: Masarykova univerzita, Nekonvenční monetární politika a instituce trhu práce pohledem dynamických stochastických modelů všeobecné rovnováhy, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

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, s. 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  - JOUR
ID  - 1435578
AU  - Chalmovianský, Jakub
PY  - 2018
TI  - What drives the estimation results of DSGE models? Effect of the input data on parameter estimates
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
UR  - https://mme2018.fm.vse.cz/
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, s.~169-174. ISBN~978-80-7378-372-3.

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