TITLE:
Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing
AUTHORS:
Stanley Jere, Mubita Siyanga
KEYWORDS:
Inflation, Holt’s Exponential Smoothing, Forecasting, Consumer Price Index, Mean Square Error and Mean Absolute Percentage Error
JOURNAL NAME:
Open Journal of Statistics,
Vol.6 No.2,
April
27,
2016
ABSTRACT: In this paper, the Holt’s exponential smoothing and
Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast
inflation rate of Zambia using the monthly consumer price index (CPI) data from
May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate
model which best fits the CPI time series data and is therefore suitable for
forecasting CPI and subsequently the inflation rate. However, the choice of the
Holt’s exponential smoothing is as good as an ARIMA model considering the
smaller deviations in the mean absolute percentage error and mean square error.
Moreover, the Holt’s exponential smoothing model is less complicated since you
do not require specialised software to implement it as is the case for ARIMA
models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6
respectively.