Understanding inflation dynamics in the Kingdom of Eswatini: a univariate approach

NYONI, THABANI and MUTONGI, CHIPO and NYONI, MUNYARADZI and HAMADZIRIPI, OSCAR HAPANYENGWI (2019): Understanding inflation dynamics in the Kingdom of Eswatini: a univariate approach.

Abstract

This research uses annual time series data on inflation rates in the Kingdom of Eswatini from 1966 to 2017, to model and forecast inflation using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the H series is I (1). The study presents the ARIMA (0, 1, 1) model for predicting inflation in the Kingdom of Eswatini. The diagnostic tests further imply that the presented optimal model is actually stable and acceptable for predicting inflation in the Kingdom of Eswatini. The results of the study apparently show that inflation in the Kingdom of Eswatini is likely to continue on an upwards trajectory in the next decade. The study basically encourages policy makers to make use of tight monetary and fiscal policy measures in order to control inflation in the Kingdom of Eswatini.

• Item Type: MPRA Paper
• Language: English
• Keywords: Eswatini; forecasting; inflation
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level ; Inflation ; Deflation; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E47 - Forecasting and Simulation: Models and Applications

References

[1] Ayub, G., Rehman, N. U., Iqbal, M., Zaman, Q & Atif, M (2014). Relationship between inflation and interest rate: evidence from Pakistan, Research Journal of Recent Sciences, 3 (4): 51 – 55.

[2] Blanchard, O (2000). Macroeconomics, 2nd Edition, Prentice Hall, New York.

[3] Box, G. E. P & Jenkins, G. M (1976). Time Series Analysis: Forecasting and Control, Holden Day, San Francisco.

[4] Brocwell, P. J & Davis, R. A (2002). Introduction to Time Series and Forecasting, Springer, New York.

[5] Buelens, C (2012). Inflation modeling and the crisis: assessing the impact on the performance of different forecasting models and methods, European Commission, Economic Paper No. 451.

[6] Chatfield, C (2004). The Analysis of Time Series: An Introduction, 6th Edition, Chapman & Hall, New York.

[7] Cryer, J. D & Chan, K. S (2008). Time Series Analysis with Application in R, Springer, New York.

[8] Enke, D & Mehdiyev, N (2014). A Hybrid Neuro-Fuzzy Model to Forecast Inflation, Procedia Computer Science, 36 (2014): 254 – 260.

[9] Fenira, M (2014). Democracy: a determinant factor in reducing inflation, International Journal of Economics and Financial Issues, 4 (2): 363 – 375.

[10] Hector, A & Valle, S (2002). Inflation forecasts with ARIMA and Vector Autoregressive models in Guatemala, Economic Research Department, Banco de Guatemala.

[11] Hurtado, C., Luis, J., Fregoso, C & Hector, J (2013). Forecasting Mexican Inflation Using Neural Networks, International Conference on Electronics, Communications and Computing, 2013: 32 – 35.

[12] Islam, R., Ghani, A. B. A., Mahyudin, F., & Manickam, N (2017). Determinants of factors affecting inflation in Malaysia, International Journal of Economics and Financial Issues, 7 (2): 355 – 364.

[13] Khumalo, L. C., Mutambara, E., & Kodua, A. A (2017). Relationship between inflation and interest rates in Swaziland revisited, Banks and Banks Systems, 12 (4): 218 – 226.

[14] King, M (2005). Monetary Policy: Practice Ahead of Theory, Bank of England.

[15] Mcnelis, P. D & Mcadam, P (2004). Forecasting Inflation with Think Models and Neural Networks, Working Paper Series, European Central Bank.

[16] Mkhatshwa, Z. S., Tijani, A. A., & Masuku, M. B (2015). Analysis of the relationship between inflation, economic growth and agricultural growth in Swaziland from 1980 – 2013, Journal of Economics and Sustainable Development, 6 (18): 189 – 204.

[17] Nyoni, T & Nathaniel, S. P (2019). Modeling Rates of Inflation in Nigeria: An Application of ARMA, ARIMA and GARCH models, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 91351.

[18] Nyoni, T (2018). Modeling and Forecasting Inflation in Zimbabwe: a Generalized Autoregressive Conditionally Heteroskedastic (GARCH) approach, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 88132.

[19] Nyoni, T (2018). Modeling and Forecasting Naira / USD Exchange Rate in Nigeria: a Box – Jenkins ARIMA approach, University of Munich Library – Munich Personal RePEc Archive (MPRA), Paper No. 88622.

[20] Nyoni, T (2018). Modeling and Forecasting Inflation in Kenya: Recent Insights from ARIMA and GARCH analysis, Dimorian Review, 5 (6): 16 – 40.

[21] Nyoni, T. (2018). Box – Jenkins ARIMA Approach to Predicting net FDI inflows in Zimbabwe, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 87737.

[22] Ramlan, H & Suhaimi, M. S. I. B (2017). The relationship between interest rates and inflation toward the economic growth in Malaysia, Journal of Humanities, Language, Culture and Business, 1 (1): 55 – 63.

[23] Salami, F. K (2018). Effect of interest rate on economic growth: Swaziland as a case study, J. Bus. Fin. Aff., 7 (3): 1 – 5.

[24] Sarangi, P. K., Sinha, D., Sinha, S & Sharma, M (2018). Forecasting Consumer Price Index using Neural Networks models, Innovative Practices in Operations Management and Information Technology – Apeejay School of Management, pp: 84 – 93.

[25] Wei, W. S (2006). Time Series Analysis: Univariate and Multivariate Methods, 2nd Edition, Pearson Education Inc, Canada.