Author: Hyun Hak Kim(Bank of Korea)
Forecast combinations and density forecast have frequently been found in empirical research to produce better prediction performance on average than methods based on the best single model. Density forecast - an estimate of the probability distribution of the possible future values of that variable - has received attention in the forecast literature.
This paper combines point forecast and density forecast to predict Korean CPI inflation and compares the performance of each forecast with various models including factor models, shrinkage models, and bayesian model averaging. We find that the more models included in point forecast combinations leads to the better performance of the combinations than the benchmark autoregressive model, regardless of the independent performance of a single model. We also find that combinations of more models provide a result robust to sample periods. Density forecasts and their combinations present the direction of future inflation and predictive densities.
We expect that forecast combination and density forecast can provide better performance with more disciplines, for example, combining more various models and mixing different frequency data models.