Back
 AJIBM  Vol.9 No.4 , April 2019
Modeling and Forecasting of Ghana’s Inflation Volatility
Abstract:
In this paper, we assessed volatility of Ghana’s inflation rates for 2000 to 2018 using the auto-regressive conditionally heteroskedasticity (ARCH), generalized ARCH (GARCH), and the exponential GARCH (EGARCH) models. The inflation data were obtained from the Ghana Statistical Service (GSS). The proposed model should be able to provide projections of inflation volatility from 2019 and beyond. The results showed that higher order models are required to properly explain Ghana’s inflation volatility and the EGARCH(12, 1) is the best fitting model for the data. The EGARCH(12, 1) model is robust to model and forecast volatility of inflation rates. Also, the results suggest that we are forecasting increasing volatility and there is increasing trend in general prices of goods and services for 2018 and beyond. The forecasts figures revealed that Ghana’s economy is likely to be unstable in 2018 and 2019. This study therefore recommends that policy makers and industry players need to put in place stringent monetary and fiscal policies that would put the anticipated increase in inflation under control. The models were implemented using R software.
Cite this paper: Iddrisu, A. , Otoo, D. , Abdul, I. and Ankamah, S. (2019) Modeling and Forecasting of Ghana’s Inflation Volatility. American Journal of Industrial and Business Management, 9, 930-949. doi: 10.4236/ajibm.2019.94064.
References

[1]   Webster, N., McKechnie, J.L., et al. (1983) Webster’s New Universal Unabridged Dictionary. Dorset & Baber, Riverside, NJ.

[2]   Hall, R.E. (1982) Introduction to “Inflation: Causes and Effects”. In: Inflation: Causes and Effects, National Bureau of Economic Research, Inc., MA, 1-10.

[3]   Hendry, D.F. (2001) Modelling UK Inflation, 1875-1991. Journal of Applied Econometrics, 16, 255-275. https://doi.org/10.1002/jae.615

[4]   Nortey, E.N.N., Mbeah-Baiden, B., Dasah, J.B. and Mettle, F.O. (2014) Modelling Rates of Inflation in Ghana: An Application of Arch Models. Current Research Journal of Economic Theory, 6, 16-21. https://doi.org/10.19026/crjet.6.5532

[5]   Chinomona, A. (2010) Time Series Modelling with Application to South African Inflation data. Master’s Thesis, University of KwaZulu-Natal, KwaZulu-Natal, South Africa.

[6]   Engle, R.F. (1982) Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50, 987-1007. https://doi.org/10.2307/1912773

[7]   Engle. R.F. (1983) Estimates of the Variance of U.S. Inflation Based Upon the ARCH Model. Journal of Money, Credit and Banking, 15, 286-301. https://doi.org/10.2307/1992480

[8]   Bera, A.K. and Higgins, M.L. (1993) ARCH Model: Properties, Estimation and Testing. Journal of Economic Surveys, 7, 305-366. https://doi.org/10.1111/j.1467-6419.1993.tb00170.x

[9]   Bollerslev, T. (1986) Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1

[10]   Nelson, D.B. (1991) Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59, 347-370. https://doi.org/10.2307/2938260

[11]   Abledu, G.K. and Agbodah, K. (2012) Stochastic Forecasting and Modeling of Volatility of Oil Prices in Ghana Using ARIMA Time Series Model. European Journal of Business and Management, 4, 122-131.

[12]   Suleman, N. and Sarpong, S. (2012) Empirical Approach to Modelling and Forecasting Inflation in Ghana. Journal of Economic Theory, 4, 83-87.

[13]   de Brouwer, G. and Ericsson, N.R. (1998) Modeling Inflation in Australia. Journal of Business & Economic Statistics, 16, 433-449. https://doi.org/10.1080/07350015.1998.10524783

[14]   Diouf, M.A. (2007) Modeling Inflation for Mali. Volume 2007. International Monetary Fund.
https://doi.org/10.5089/9781451868586.001

[15]   Vizek, M. and Broz, T. (2009) Modeling Inflation in Croatia. Emerging Markets Finance and Trade, 45, 87-98. https://doi.org/10.2753/REE1540-496X450606

[16]   Stock, J.H. and Watson, M.W. (2010) Modeling Inflation after the Crisis. Technical Report, National Bureau of Economic Research.

[17]   R Core Team, et al. (2013) R: A Language and Environment for Statistical Computing.

[18]   R Core Team, et al. (2014) R: A Language and Environment for Statistical Computing.

[19]   R Core Team (2016) Vienna: R Foundation for Statistical Computing. 2016. R: A Language and Environment for Statistical Computing.

[20]   Royston, P. (1993) A Pocket-Calculator Algorithm for the Shapiro-Francia Test for Non-Normality: An Application to Medicine. Statistics in Medicine, 12, 181-184.
https://doi.org/10.1002/sim.4780120209

[21]   Thode, H.C. (2002) Testing for Normality. CRC Press, Boca Raton, FL.
https://doi.org/10.1201/9780203910894

[22]   Dickey, D.A. and Fuller, W.A. (1979) Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74, 427-431.
https://doi.org/10.1080/01621459.1979.10482531

[23]   Said, S.E. and Dickey, D.A. (1984) Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order. Biometrika, 71, 599-607. https://doi.org/10.1093/biomet/71.3.599

[24]   Kwiatkowski, D., Phillips, P.C.B., Schmidt, P. and Shin, Y.C. (1992) Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root: How Sure Are We that Economic Time Series Have a Unit Root? Journal of Econometrics, 54, 159-178. https://doi.org/10.1016/0304-4076(92)90104-Y

[25]   Becker, R. (2018) The New S Language. CRC Press, Boca Raton, FL.

[26]   French, K.R., Schwert, G.W. and Stambaugh, R.F. (1987) Expected Stock Returns and Volatility. Journal of Financial Economics, 19, 3-29. https://doi.org/10.1016/0304-405X(87)90026-2

[27]   Engle III, R.F., Ito, T. and Lin, W.-L. (1988) Meteor Showers or Heat Waves? Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market. Econometrica, 58, 525-542.

[28]   Baillie, R.T. and Bollerslev, T. (2002) The Message in Daily Exchange Rates: A Conditional-Variance Tale. Journal of Business & Economic Statistics, 20, 60-68.
https://doi.org/10.1198/073500102753410390

[29]   Geweke, J. (1986) Exact Inference in the Inequality Constrained Normal Linear Regression Model. Journal of Applied Econometrics, 1, 127-141. https://doi.org/10.1002/jae.3950010203

[30]   Milhoj, A. (1987) A Multiplicative Parameterization of ARCH Models. University of Copenhagen, Copenhagen, Denmark.

[31]   Javed, F. and Mantalos, P. (2013) Garch-Type Models and Performance of Information Criteria. Communications in Statistics-Simulation and Computation, 42, 1917-1933.

 
 
Top