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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F18%3A00103666" target="_blank" >RIV/00216224:14560/18:00103666 - isvavai.cz</a>

Výsledek na webu

<a href="https://mme2018.fm.vse.cz/" target="_blank" >https://mme2018.fm.vse.cz/</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 36th International Conference Mathematical Methods in Economics

ISBN

9788073783723

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

169-174

Název nakladatele

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

Místo vydání

Prague

Místo konání akce

Jindřichův Hradec, Czech Republic

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000507455300030