Modeling And Forecasting Ghana’s Inflation Rate Under Threshold Models

Emmanuel Antwi, Emmanuel Numapau Gyamfi and Kwabena A. Kyei ()
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Kwabena A. Kyei: University of Venda

Journal of Developing Areas, 2019, vol. 53, issue 3, 93-105

Abstract: In this study, we model and forecast Ghana’s inflation rate using nonlinear models between the period January 1981 and August 2016. Nonlinearity tests were conducted on the logarithm of the monthly rates of inflation using Keenan and Tsay tests, and based on the results, we rejected the null hypothesis of linearity of monthly rates of inflation. We used threshold models and compared their fitness and forecasting performance with standard linear Autoregressive (AR) models. We found out that the Self-Exiting Threshold Autoregressive (SETAR) and Logistic Smooth Threshold Autoregressive (LSTAR) models fit the data better. The simple linear Autoregressive (AR) models however, out-performed the nonlinear models in terms of forecasting. Various research projects have been carried out in this area of inflation modeling in Ghana, but these researchers modeled inflation in Ghana using nonlinear models that did not account for the conditional heteroscedasticity in the model. These models have been used and empirical evidence of their relative performance has been given for the success of developed economies such as US and Europe. However, limited studies have been done in the case of Ghana. This indicates a gap in the literature and poses a challenge as to which of these models is the optimal choice for modeling economic and financial data such as inflation rates for developing countries. It is recommended that policy makers, industry players and all those interested in modeling future rates of inflation in Ghana should consider using the threshold models instead of the traditional Box and Jenkins models since the threshold models are able to capture the heteroscedasticity in the model. Also by using the threshold models, policy makers and industry players would be able to properly capture the variability persistence in the monthly rates of inflation and hence estimates would be more accurate. Lastly, from 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 made in the year 2016.

Keywords: Inflation; Nonlinear Models; Self-Exciting Threshold Autoregression Model; Logistics Smooth Threshold Autoregression Model; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:jda:journl:vol.53:year:2019:issue3:pp:93-105

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