Predictive Performance of Alternative Inflation Forecasting Models: New International Evidence
Journal of Applied Business and Economics
Inflation rate and its volatility have been at a subdued level for most industrialized and emerging countries since the mid-1990s. The objective of this study is to evaluate the predictive performance of three alternative inflation forecasting models -- univariate time-series (ARIMA) model, Phillips curve model, and naïve model -- for a selected number of inflation-targeting countries and non-inflation targeting countries over the period 1998-2015, a unique period marked by relatively low and stable inflation rate. It is found that out-of-sample inflation forecasts generated by ARIMA model are more accurate than those generated by the other two forecasting models for the majority of these countries. This study concludes, that during the period of low inflation rate, the central bank should weigh inflation forecasts obtained from a simple time-series model, such as ARIMA model, more heavily in its decisionmaking process.
Predictive Performance of Alternative Inflation Forecasting Models: New International Evidence.
Journal of Applied Business and Economics, 20(6), 161–177.