Regime-Switching, stochastic volatilty and impacts of monetary policy shocks on macroeconomic fluctuations in Peru

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This paper utilizes regime-switching VAR models with stochastic volatility (RS-VAR-SV) to analyze the impact and evolution of monetary policy shocks and their contribution to the dynamics of GDP growth, inflation, and the interest rate in Peru for the period from 1994Q3 to 2019Q4. The main findings...

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Detalles Bibliográficos
Autores: Alvarado Silva, Paola, Cáceres Quispe, Moisés, Rodríguez, Gabriel
Formato: documento de trabajo
Fecha de Publicación:2024
Institución:Pontificia Universidad Católica del Perú
Repositorio:PUCP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.pucp.edu.pe:20.500.14657/200763
Enlace del recurso:https://repositorio.pucp.edu.pe/index/handle/123456789/200763
http://doi.org/10.18800/2079-8474.0537
Nivel de acceso:acceso abierto
Materia:Regime-Switching VAR
Stochastic Volatility
Marginal Likelihood
Bayesian Models
Monetary Policy
Peru
https://purl.org/pe-repo/ocde/ford#5.02.01
Descripción
Sumario:This paper utilizes regime-switching VAR models with stochastic volatility (RS-VAR-SV) to analyze the impact and evolution of monetary policy shocks and their contribution to the dynamics of GDP growth, inflation, and the interest rate in Peru for the period from 1994Q3 to 2019Q4. The main findings are: (i) the best-fifting models incorporate only SV; (ii) there are two distinct regimes coinciding with the implementation of the inflation targeting (IT) scheme; (iii) the volatility of GDP growth and inflation began to decrease in the early 1990s, while interest rate volatility declined following IT implementation; and (iv) pre-IT, monetary policy shocks accounted for 15%, 30%, and 90% of the forecast error variance decomposition for in ation, GDP growth, and the interest rate in the long term, respectively. Following IT adoption, monetary policy ceased to be a source of uncertainty for the economy. These results are robust to changes in priors, domestic and external variables, the number of regimes, and the ordering and number of variables of the model.
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