Keywords
Inferencia bayesiana
Monte Carlo Hamiltoniano
Inflación y crecimiento del producto
Bivariate GARCH-M models
Bayesian inference
Hamiltonian Monte Carlo
Inflation and output growth
How to Cite
Abstract
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.
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