Inflation forecasts are highly important in the actual management of monetary policy implying that central banks must acquire accurate inflation forecasts. Given the high risks involved in inflation, models that account for uncertainty are expected to do well in terms of forecasting power. The current paper focuses on estimating 12 different specifications including 10 univariate models and 2 multivariate models. The univariate models are generalized autoregressive conditional heteroskedasticity in mean (GARCH-M) and threshold autoregressive generalized conditional heteroscedasticity in mean (TARCH-M) models assuming three different distributions for the error term as well as an extension of the GARCH-M model that allows for time-varying higher order moments.
Furthermore, we employed three models that take into consideration the possibility of structural shifts, namely, Markov switch, threshold autoregressive and time-varying coefficients (TVCs) models. The multivariate models are the vector- half operator model and the dynamic stochastic general equilibrium - vector autoregression model (DSGE-VAR). Results indicate that TVCs model and time-varying higher moments outperform other competing models. Finally, forecasts are improved by generating combined forecasts using equal weights, Bayesian model averaging and dynamic model averaging. |
