NYONI, THABANI (2019): Forecasting inflation in Burkina Faso using ARMA models.
PDF
MPRA_paper_92443.pdf Download (208kB) |
Abstract
This research uses annual time series data on inflation rates in Burkina Faso from 1960 to 2017, to model and forecast inflation using ARMA models. Diagnostic tests indicate that B is I(0). The study presents the ARMA (2, 0, 0) model, which is nothing but an AR (2) model. The diagnostic tests further imply that the presented optimal ARMA (2, 0, 0) model is stable and acceptable. The results of the study apparently show that W will be approximately 4% by 2020. Policy makers and the business community in Burkina Faso are expected to take advantage of the anticipated stable inflation rates over the next decade.
Item Type: | MPRA Paper |
---|---|
Original Title: | Forecasting inflation in Burkina Faso using ARMA models |
Language: | English |
Keywords: | Burkina Faso; 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 |
Item ID: | 92443 |
Depositing User: | MR. THABANI NYONI |
Date Deposited: | 03 Mar 2019 19:00 |
Last Modified: | 27 Sep 2019 09:52 |
References: | [1] Blanchard, O (2000). Macroeconomics, 2nd Edition, Prentice Hall, New York. [2] 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. [3] Fenira, M (2014). Democracy: a determinant factor in reducing inflation, International Journal of Economics and Financial Issues, 4 (2): 363 – 375. [4] Hector, A & Valle, S (2002). Inflation forecasts with ARIMA and Vector Autoregressive models in Guatemala, Economic Research Department, Banco de Guatemala. [5] Jesmy, A (2010). Estimation of future inflation in Sri Lanka using ARMA model, Kalam Journal, V: 21 – 27. [6] Nyoni, T & Bonga, W. G (2017). Population Growth in Zimbabwe: A Threat to Economic Development? DRJ – Journal of Economics and Finance, 2 (6): 26 – 39. [7] 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. [8] 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. [9] Nyoni, T (2018). Modeling Forecasting Naira / USD Exchange Rate in Nigeria: a Box – Jenkins ARIMA approach, University of Munich Library – Munich Personal RePEc Archive (MPRA), Paper No. 88622. [10] Nyoni, T (2018). Modeling and Forecasting Inflation in Kenya: Recent Insights from ARIMA and GARCH analysis, Dimorian Review, 5 (6): 16 – 40. [11] Osarumwense, O. I & Waziri, E. I (2013). Modeling monthly inflation rate volatility, using Generalized Autoregressive Conditionally Heteroskedastic (GARCH) models: evidence from Nigeria, Australian Journal of Basic and Applied Sciences, 7 (7): 991 – 998. [12] Popoola, O. P., Ayanrinde, A. W., Rafiu, A. A & Odusina, M. T (2017). Time series analysis to model and forecast inflation rate in Nigeria, Anale. Seria. Informatica., XV (1): 174 – 178. [13] Stovicek, K (2007). Forecasting with ARMA models: The case of Slovenia inflation, Bank of Slovenia, pp: 23 – 56. [14] Walgenbach, P. H., Dittrich, N. E & Hunson, E. I (1973). Financial Accounting, Harcout Brace Javonvich, New York. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/92443 |