NYONI, THABANI (2019): ARIMA modeling and forecasting of inflation in Egypt (1960-2017).
PDF
MPRA_paper_92446.pdf Download (213kB) |
Abstract
This research uses annual time series data on inflation rates in Egypt from 1960 to 2017, to model and forecast inflation using ARIMA models. Diagnostic tests indicate that E is I(1). The study presents the ARIMA (0, 1, 1). The diagnostic tests further imply that the presented optimal ARIMA (0, 1, 1) model is stable and acceptable for predicting inflation in Egypt. The results of the study apparently show that E will be approximately 23.3% over the out-of-sample forecast period. The CBE is expected to continue tightening Egypt’s monetary policy in order to restore price stability.
Item Type: | MPRA Paper |
---|---|
Original Title: | ARIMA modeling and forecasting of inflation in Egypt (1960-2017) |
Language: | English |
Keywords: | Egypt; 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: | 92446 |
Depositing User: | MR. THABANI NYONI |
Date Deposited: | 03 Mar 2019 18:59 |
Last Modified: | 28 Sep 2019 17:08 |
References: | [1] Blanchard, O (2000). Macroeconomics, 2nd Edition, Prentice Hall, New York. [2] BNP PARIBAS (2018). Egypt – Gradual monetary easing, PNP PARIBUS. [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] Hector, A & Valle, S (2002). Inflation forecasts with ARIMA and Vector Autoregressive models in Guatemala, Economic Research Department, Banco de Guatemala. [10] Hosny, A. S (2016). What is the Central Bank of Egypt’s implicit inflation target? International Journal of Applied Economics, 13 (1): 43 – 56. [11] King, M (2005). Monetary Policy: Practice Ahead of Theory, Bank of England. [12] Moriyama, K (2011). Inflation inertia in Egypt and its policy implications, Middle East and Central Asia Department, IMF. [13] 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. [14] Nyoni, T (2018). Modeling and Forecasting Inflation in Kenya: Recent Insights from ARIMA and GARCH analysis, Dimorian Review, 5 (6): 16 – 40. [15] 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. [16] 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. [17] 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. [18] Wei, W. S (2006). Time Series Analysis: Univariate and Multivariate Methods, 2nd Edition, Pearson Education Inc, Canada. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/92446 |