NYONI, THABANI (2019): Inflation dynamics in Jamaica: Evidence from the ARMA methodology.
PDF
MPRA_paper_92449.pdf Download (245kB) |
Abstract
This research uses annual time series data on inflation rates in Jamaica from 1968 to 2017, to model and forecast inflation using ARMA models. Diagnostic tests indicate that JINF is I(0). The study presents the ARMA (1, 0, 0) model, which is the same as an AR (1) process. The diagnostic tests further imply that the presented optimal ARMA (1, 0, 0) model is stable and acceptable for forecasting inflation rates in Jamaica. The results of the study apparently show that JINF will be approximately 11.42% by 2020. Policy makers in Jamaica are expected to the take the necessary action with regards to maintaining a low and stable inflation rate over the next decade and even beyond.
Item Type: | MPRA Paper |
---|---|
Original Title: | Inflation dynamics in Jamaica: Evidence from the ARMA methodology |
Language: | English |
Keywords: | Forecasting; inflation; Jamaica |
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: | 92449 |
Depositing User: | MR. THABANI NYONI |
Date Deposited: | 03 Mar 2019 18:58 |
Last Modified: | 27 Sep 2019 04:47 |
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 & 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. [7] 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. [8] Nyoni, T (2018). Modeling and Forecasting Inflation in Kenya: Recent Insights from ARIMA and GARCH analysis, Dimorian Review, 5 (6): 16 – 40. [9] 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. [10] 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. [11] Stovicek, K (2007). Forecasting with ARMA models: The case of Slovenia inflation, Bank of Slovenia, pp: 23 – 56. [12] Walgenbach, P. H., Dittrich, N. E & Hunson, E. I (1973). Financial Accounting, Harcout Brace Javonvich, New York. [13] Whyte, S (2011). Modeling inflation rate in Jamaica: the role of monetary indicators, Bank of Jamaica, Research Paper 2011/08. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/92449 |