Forecasting Czech GDP using Bayesian dynamic model averaging EL Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F26482789%3A_____%2F18%3AN0000003" target="_blank" >RIV/26482789:_____/18:N0000003 - isvavai.cz</a>

Výsledek na webu

<a href="https://iises.net/international-journal-of-economic-sciences/publication-detail-1721" target="_blank" >https://iises.net/international-journal-of-economic-sciences/publication-detail-1721</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.20472/ES.2018.7.1.004" target="_blank" >10.20472/ES.2018.7.1.004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting Czech GDP using Bayesian dynamic model averaging EL Classification

Popis výsledku v původním jazyce

Forecasting future path of macroeconomic aggregates has become crucial for monetary and fiscal policymakers. Using Czech data, the aim of this paper is to demonstrate the benefits of the Bayesian dynamic averaging and Bayesian Vector Autoregressive Models (BVAR) in forecasting real GDP growth. Estimation of richly parameterized VARs often leads to unstable estimates and inaccurate forecasts in models with many variables. Bayesian inference and proper choice of informative priors offers an effective solution to this problem by shrinking the variance of model parameters. Bayesian dynamic model averaging (DMA) then makes it possible to account for model uncertainty by combining predictive abilities of many competing VAR models considered by a researcher. Since forecasting performance of individual models may vary over time, the DMA can adapt their weights in dynamic and optimal way. It is shown that the application of DMA leads to substantial forecasting gains in forecasting Czech real GDP.

Název v anglickém jazyce

Forecasting Czech GDP using Bayesian dynamic model averaging EL Classification

Popis výsledku anglicky

Forecasting future path of macroeconomic aggregates has become crucial for monetary and fiscal policymakers. Using Czech data, the aim of this paper is to demonstrate the benefits of the Bayesian dynamic averaging and Bayesian Vector Autoregressive Models (BVAR) in forecasting real GDP growth. Estimation of richly parameterized VARs often leads to unstable estimates and inaccurate forecasts in models with many variables. Bayesian inference and proper choice of informative priors offers an effective solution to this problem by shrinking the variance of model parameters. Bayesian dynamic model averaging (DMA) then makes it possible to account for model uncertainty by combining predictive abilities of many competing VAR models considered by a researcher. Since forecasting performance of individual models may vary over time, the DMA can adapt their weights in dynamic and optimal way. It is shown that the application of DMA leads to substantial forecasting gains in forecasting Czech real GDP.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

International Journal of Economic Sciences

ISSN

1804-9796

e-ISSN

—

Svazek periodika

7/2018

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

17

Strana od-do

65-81

Kód UT WoS článku

000432932500004

EID výsledku v databázi Scopus

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