Abstract:
Over the years researchers have been modeling inflation rate in Ghana using linear models such as
Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average (ARMA) and
Moving Average (MA). Empirical research however, has shown that financial data, such as inflation rate,
does not follow linear patterns. This study seeks to model and forecast inflation in Ghana using nonlinear
models and to establish the existence of nonlinear patterns in the monthly rates of inflation between
the period January 1981 to August 2016 as obtained from Ghana Statistical Service. Nonlinearity tests
were conducted using Keenan and Tsay tests, and based on the results, we rejected the null hypothesis
of linearity of monthly rates of inflation. The Augmented Dickey-Fuller (ADF) was performed to test for
the presence of stationarity. The test rejected the null Hypothesis of unit root at 5% significant level,
and hence we can conclude that the rate of inflation was stationary over the period under consideration.
The data were transformed by taking the logarithms to follow nornal distribution, which is a desirable
characteristic feature in most time series. Monthly rates of inflation were modeled using threshold
models and their fitness and forecasting performance were compared with Autoregressive (AR ) models.
Two Threshold models: Self-Exciting Threshold Autoregressive (SETAR) and Logistic Smooth Threshold
Autoregressive (LSTAR) models, and two linear models: AR(1) and AR(2), were employed and fitted
to the data. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)
were used to assess each of the fitted models such that the model with the minimum value of AIC
and BIC, was judged the best model. Additionally, the fitted models were compared according to their
forecasting performance using a criterion called mean absolute percentage error (MAPE). The model
with the minimum MAPE emerged as the best forecast model and then the model was used to forecast
monthly inflation rates for the year 2017.
The rationale for choosing this type of model is contingent on the behaviour of the time-series data.
Also with the history of inflation modeling and forecasting, nonlinear models have proven to perform
better than linear models.
The study found that the SETAR and LSTAR models fit the data best. The simple AR models however,
out-performed the nonlinear models in terms of forecasting. Lastly, looking at the upward trend of the
out-sample forecasts, it can be predicted that Ghana would experience double digit inflation in 2017.
This would have several impacts on many aspects of the economy and could erode the economic gains
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made in the year 2016. Our study has important policy implications for the Central Bank of Ghana which
can use the data to put in place coherent monetary and fiscal policies that would put the anticipated
increase in inflation under control.
