Odhad stavů v DSGE modelech: Kalmanův filtr nebo Bootstrapový filtr?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F08%3A00026575" target="_blank" >RIV/00216224:14560/08:00026575 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

States Estimation in Baseline New Keynesian DSGE Model: Kalman or Bootstrap Filter?

Popis výsledku v původním jazyce

The aim of this article is to compare the ability of estimation and filtering methods. Under assumptions of normality and linear structure, Kalman filter seems to be more accurate in comparison with Bootstrap filter that cooperates with empirical distributions. But we can show that 'chances' are approximately similar if we set for Bootstrap filter the same variances of structural shocks as we obtain from Kalman smoothed structural shocks. A construction of Kalman filter enables to flexibly change estimated covariance matrix of observation vector estimation error during filtering and smoothing procedure. But this 'advantage' is not implemented in Bootstrap filter since the bootstrap filter measurement error variances and structural shocks variances mustbe constant during filtering and smoothing procedure. The results will be shown on the DSGE baseline new Keynesian model of Czech economy. The model will be filtered and smoothed by above mentioned filters and a comparison study will be

Název v anglickém jazyce

States Estimation in Baseline New Keynesian DSGE Model: Kalman or Bootstrap Filter?

Popis výsledku anglicky

The aim of this article is to compare the ability of estimation and filtering methods. Under assumptions of normality and linear structure, Kalman filter seems to be more accurate in comparison with Bootstrap filter that cooperates with empirical distributions. But we can show that 'chances' are approximately similar if we set for Bootstrap filter the same variances of structural shocks as we obtain from Kalman smoothed structural shocks. A construction of Kalman filter enables to flexibly change estimated covariance matrix of observation vector estimation error during filtering and smoothing procedure. But this 'advantage' is not implemented in Bootstrap filter since the bootstrap filter measurement error variances and structural shocks variances mustbe constant during filtering and smoothing procedure. The results will be shown on the DSGE baseline new Keynesian model of Czech economy. The model will be filtered and smoothed by above mentioned filters and a comparison study will be

Druh

D - Stať ve sborníku

CEP obor

AH - Ekonomie

OECD FORD obor

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Projekt

<a href="/cs/project/1M0524" target="_blank" >1M0524: Centrum výzkumu konkurenční schopnosti české ekonomiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2008

Kód důvěrnosti údajů

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

Název statě ve sborníku

Mathematical Methods in Economics 2008

ISBN

978-80-7372-387-3

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

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Název nakladatele

Technical University of Liberec

Místo vydání

Liberec

Místo konání akce

Liberec

Datum konání akce

1. 1. 2008

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000260962300060