A bayesian estimation of Bivariate Garch-M Models

 

שמור ב:
מידע ביבליוגרפי
מחבר: Cruz Torres, Cristian
פורמט: artículo original
סטטוס:Versión publicada
Fecha de Publicación:2024
תיאור:The generalized autoregressive conditional heteroskedasticity (GARCH) model is a statistical model for time series used to describes the variance of the current error as a function of past squared errors terms and previous variances. These GARCH models are commonly used in modeling time varying volatility and volatility clustering. If, in addition, the effect of the variance is included in the observations to predict the mean, we have the GARCH-M (GARCH in mean) models. In this paper, the above issues are analyzed in a bayesian approach to modeling a bivariate time series, where the observations is assumed to behave as a VAR-GARCH-M model. An application of a bivariate model is fitted to measure the effects of inflation variability and uncertainty growth on inflation and output growth mean.
País:Portal de Revistas UCR
מוסד:Universidad de Costa Rica
Repositorio:Portal de Revistas UCR
שפה:Español
OAI Identifier:oai:portal.ucr.ac.cr:article/53186
גישה מקוונת:https://revistas.ucr.ac.cr/index.php/matematica/article/view/53186
Access Level:acceso abierto
מילת מפתח:Modelos bivariados GARCH-M
Inferencia bayesiana
Monte Carlo Hamiltoniano
Inflación y crecimiento del producto
Bivariate GARCH-M models
Bayesian inference
Hamiltonian Monte Carlo
Inflation and output growth