NYONI, THABANI (2019): Forecasting UK consumer price index using Box-Jenkins ARIMA models.
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Abstract
This research uses annual time series data on CPI in the UK from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the K series is I (2). The study presents the ARIMA (1, 2, 1) model for predicting CPI in the UK. The diagnostic tests further indicate that the presented optimal model is actually stable and acceptable. The results of the study apparently show that CPI in the UK is likely to continue on a sharp 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 UK.
Item Type: | MPRA Paper |
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Original Title: | Forecasting UK consumer price index using Box-Jenkins ARIMA models |
Language: | English |
Keywords: | Forecasting; Inflation; UK |
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: | 92410 |
Depositing User: | MR. THABANI NYONI |
Date Deposited: | 28 Feb 2019 10:12 |
Last Modified: | 11 Oct 2019 09:44 |
References: | [1] Boskin, M. J., Ellen, R. D., Gordon, R. J., Grilliches, Z & Jorgenson, D. W (1998). Consumer Price Index and the Cost of Living, The Journal of Economic Perspectives, 12 (1): 3 – 26. [2] Box, G. E. P & Jenkins, G. M (1976). Time Series Analysis: Forecasting and Control, Holden Day, San Francisco. [3] Brocwell, P. J & Davis, R. A (2002). Introduction to Time Series and Forecasting, Springer, New York. [4] Chatfield, C (2004). The Analysis of Time Series: An Introduction, 6th Edition, Chapman & Hall, New York. [5] Cryer, J. D & Chan, K. S (2008). Time Series Analysis with Application in R, Springer, New York. [6] Du, Y., Cai, Y., Chen, M., Xu, W., Yuan, H & Li, T (2014). A novel divide-and-conquer model for CPI prediction using ARIMA, Gray Model and BPNN, Procedia Computer Science, 31 (2014): 842 – 851. [7] Enke, D & Mehdiyev, N (2014). A Hybrid Neuro-Fuzzy Model to Forecast Inflation, Procedia Computer Science, 36 (2014): 254 – 260. [8] 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. [9] Kharimah, F., Usman, M., Elfaki, W & Elfaki, F. A. M (2015). Time Series Modelling and Forecasting of the Consumer Price Bandar Lampung, Sci. Int (Lahore)., 27 (5): 4119 – 4624. [10] Kock, A. B & Terasvirta, T (2013). Forecasting the Finnish Consumer Price Inflation using Artificial Network Models and Three Automated Model Section Techniques, Finnish Economic Papers, 26 (1): 13 – 24. [11] Manga, G. S (1977). Mathematics and Statistics for Economics, Vikas Publishing House, New Delhi. [12] Mcnelis, P. D & Mcadam, P (2004). Forecasting Inflation with Think Models and Neural Networks, Working Paper Series, European Central Bank. [13] Meyler, A., Kenny, G & Quinn, T (1998). Forecasting Irish Inflation using ARIMA models, Research and Publications Department, Central Bank of Ireland. [14] 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. [15] Nyoni, T (2018k). 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 (2018l). 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. [17] Nyoni, T (2018n). Modeling and Forecasting Inflation in Kenya: Recent Insights from ARIMA and GARCH analysis, Dimorian Review, 5 (6): 16 – 40. [18] Nyoni, T. (2018i). Box – Jenkins ARIMA Approach to Predicting net FDI inflows in Zimbabwe, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 87737. [19] 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. [20] Subhani, M. I & Panjwani, K (2009). Relationship between Consumer Price Index (CPI) and Government Bonds, South Asian Journal of Management Sciences, 3 (1): 11 – 17. [21] 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/92410 |