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Issue 5(1), October 2010 -- Paper Abstracts
Girard  (p. 9-22)
Cooper (p. 23-32)
Kunz-Osborne (p. 33-41)
Coulmas-Law (p.42-46)
Stasio (p. 47-56)
Albert-Valette-Florence (p.57-63)
Zhang-Rauch (p. 64-70)
Alam-Yasin (p. 71-78)
Mattare-Monahan-Shah (p. 79-94)
Nonis-Hudson-Hunt (p. 95-106) 



JOURNAL OF APPLIED BUSINESS AND ECONOMICS


Predictive Performance of Alternative Inflation Forecasting Models: New International Evidence


Author(s): Unro Lee

Citation: Unro Lee, (2018) "Predictive Performance of Alternative Inflation Forecasting Models: New International Evidence"," Journal of Applied Business and Economics, Vol. 20, Iss.6,  pp. 161-177

Article Type: Research paper

Publisher: North American Business Press

Abstract:

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.