Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models
##plugins.themes.bootstrap3.article.main##
Price stability is the primary monetary policy objective in any economy since it protects the interests of both consumers and producers. As a result, forecasting is a common practice and a vital aspect of monetary policymaking. Future predictions guide monetary and fiscal policy tools that that be used to stabilize commodity prices. As a result, developing an accurate and precise forecasting model is critical. The current study fitted and forecasted the food and beverages price index (FBPI) in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. Unlike other ARIMA models like the autoregressive (AR), Moving Average (MA), and non-seasonal ARMA models, the SARIMA model accounts for the seasonal component in a given time series data better forecasts. The study relied on secondary data obtained from the KNBS website on monthly food and beverage price index in Kenya from January 1991 to February 2020. R-statistical software was used to analyze the data. The parameter estimation was done using the Maximum Likelihood Estimation method. Competing SARIMA models were compared using the Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE),.and Mean Absolute Percentage Error (MAPE). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The forecasting ability evaluation statistics MAE = 2.00%, MAPE = 1.62% and MASE = 0.87%. The 24-step ahead forecasts showed that the FPBI is unstable with an overall increasing trend. Therefore, the monetary policy committee ought to control inflation through monetary or fiscal policy, strengthening food security and trade liberalization.
References
-
Smith JL. World oil: market or mayhem? Journal of Economic Perspectives. 2009; 23(3): 145-64.
Google Scholar
1
-
Misati RN, Nyamongo EM, Mwangi I. Commodity price shocks and inflation in a net oil‐importing economy. OPEC Energy Review. 2013; 37(2): 125-48.
Google Scholar
2
-
Maxwell Fry DJ, Lavan Mahadeva SR, Sterne G. Key issues in the choice of monetary policy framework. Monetary policy frameworks in a global context. 2000; 2.
Google Scholar
3
-
Ngwen N, Amba Oyon CM, Mbratana T. Government expense, Consumer Price Index and Economic Growth in Cameroon.
Google Scholar
4
-
Knaut A, Paschmann M. Price volatility in commodity markets with restricted participation. Energy Economics. 2019; 81: 37-51.
Google Scholar
5
-
Nathan MM, Jagongo A. Monetary policy tools and inflation in Kenya. Money. 2017; 7(1): 86-97.
Google Scholar
6
-
Laxton D, Rose D, Scott AM. Developing a Structured Forecasting and Policy Analysis System to Support Inflation-Forecast Targeting (IFT).
Google Scholar
7
-
CBK. Central Bank of Kenya. Monetary Policy Statement, CBK, Nairobi, 2017.
Google Scholar
8
-
Headey D, Fan S. Reflections on the global food crisis: How did it happen? How has it hurt? And how can we prevent the next one?. International Food Policy Research Institute. 2010.
Google Scholar
9
-
Brida JG, Garrido N. Tourism forecasting using SARIMA models in Chilean regions. International Journal of Leisure and Tourism Marketing. 2011; 2(2): 176-90.
Google Scholar
10
-
Stoklasová R. Model of the unemployment rate in the Czech Republic. InProceedings of 30th international conference on mathematical methods in economics 2012; 836-841.
Google Scholar
11
-
Nasiru S, Sarpong S. Empirical Approach to Modelling and Forecasting Inflation in Ghana. Current Research Journal of Economic Theory. 2012; 4(3): 83-87.
Google Scholar
12
-
Adanacioglu H, Yercan M. An analysis of tomato prices at wholesale level in Turkey: an application of SARIMA model. Custos e@ gronegócio on line. 2012; 8(4): 52-75.
Google Scholar
13
-
Kibunja HW, Kihoro JM, Orwa GO, Yodah WO. Forecasting precipitation using SARIMA Model: A case study of Mt. Kenya Region.
Google Scholar
14
-
Kumar M, Anand M. An application of time series ARIMA forecasting model for predicting sugarcane production in India. Studies in Business and Economics. 2014; 9(1): 81-94.
Google Scholar
15
-
Etuk EH. An additive Sarima model for daily exchange rates of the Malaysian Ringgit (MYR) and Nigerian Naira (NGN). International Journal of Empirical Finance. 2014; 2(4): 193-201.
Google Scholar
16
-
Gikungu SW, Waititu AG, Kihoro JM. Forecasting inflation rate in Kenya using SARIMA model. American Journal of Theoretical and Applied Statistics. 2015; 4(1): 15-18.
Google Scholar
17
-
Dritsaki C. Forecast of SARIMA models: Αn application to unemployment rates of Greece. American Journal of Applied Mathematics and Statistics. 2016; 4(5): 136-48.
Google Scholar
18
-
Mutwiri RM. Forecasting of tomatoes wholesale prices of Nairobi in Kenya: time series analysis using SARIMA model. Journal of Statistical Distributions and Applications. 2019; 5(3): 46-53.
Google Scholar
19
-
Suleman N, Sarpong S. Empirical approach to modeling and forecasting inflation in Ghana. Current Research Journal of Economic Theory. 2012; 4(3): 83-87.
Google Scholar
20
-
Boateng FO, Amoah-Mensah J, Anokye M, Osei L, Dzebre P. Modeling of tomato prices in Ashanti region, Ghana, using seasonal autoregressive integrated moving average model. Journal of Advances in Mathematics and Computer Science. 2017: 1-3.
Google Scholar
21
-
Otu OA, Osuji GA, Opara J, Mbachu HI, Iheagwara AI. Application of Sarima models in modelling and forecasting Nigeria's inflation rates. American Journal of Applied Mathematics and Statistics. 2014; 2(1): 16-28.
Google Scholar
22
-
Gikungu SW, Waititu AG, Kihoro JM. Forecasting inflation rate in Kenya using SARIMA model. American Journal of Theoretical and Applied Statistics. 2015; 4(1): 15-18.
Google Scholar
23
-
Omane-Adjepong M, Oduro FT, Oduro SD. Determining the better approach for short-term forecasting of ghana’s inflation: Seasonal ARIMA Vs holt-winters. International Journal of Business, Humanities and Technology. 2013; 3(1): 69-79.
Google Scholar
24
-
Lidiema C. Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing. American Journal of Theoretical and Applied Statistics. 2017; 6(3): 161-169.
Google Scholar
25
-
Baeta FD, Tumaku J, Ahiave EK. The optimal forecast model for Ghanas inflation: A stochastic approach. Journal of Economics and International Finance. 2019; 11(2): 15-23.
Google Scholar
26
-
Verbesselt J, Hyndman R, Newnham G, Culvenor D. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment. 2010; 114(1): 106-115.
Google Scholar
27
-
Wang W, Niu Z. Time series analysis of NASDAQ composite based on seasonal ARIMA model. In2009 International Conference on Management and Service Science 2009 Sep 20 (pp. 1-4). IEEE.
Google Scholar
28
-
Chen KY, Wang CH. A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Systems with Applications. 2007; 32(1): 254-264.
Google Scholar
29
-
Shumway RH, Stoffer DS. Time series regression and exploratory data analysis. Time Series Analysis and Its Applications: With R Examples. 2006: 48-83.
Google Scholar
30
-
Box GE, Jenkins GM, Reinsel GC, Ljung GM. Time series analysis: forecasting and control. 2015.
Google Scholar
31
-
RStudio Team. RStudio: Integrated Development Environment for R [Internet]. Boston, MA; 2015. Available from: http://www.rstudio.com/
Google Scholar
32
-
Dickey DA, Fuller WA. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association. 1979; 74(366a): 427-31.
Google Scholar
33
-
Box GEP, Jenkins GM, Reinsel GC. Time series analysis, forecasting and control. Englewood Clifs. 1994.
Google Scholar
34
-
Vandekerckhove J, Matzke D, Wagenmakers EJ. Model comparison and the principle of parsimony. eScholarship, University of California; 2014.
Google Scholar
35
-
Nkoro E, Uko AK. Autoregressive Distributed Lag (ARDL) cointegration technique: application and interpretation. Journal of Statistical and Econometric Methods. 2016; 5(4): 63-91.
Google Scholar
36
-
Phung D, Huang C, Rutherford S, Chu C, Wang X, Nguyen M, et al. Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam. Acta Tropica. 2015; 141: 88-96.
Google Scholar
37
-
Akaike H. On the Entropy Maximization Principle.“PR Krishniah (ed.), Applications of Statistics: 27-41. 1977.
Google Scholar
38
-
Hyndman RJ, Khandakar Y. Automatic time series for forecasting: the forecast package for R (No. 6/07). Clayton VIC, Australia: Monash University, Department of Econometrics and Business Statistics. 2007.
Google Scholar
39
-
Schwarz G. Estimating the dimension of a model. The Annals of Statistics. 1978: 461-4.
Google Scholar
40
-
McLeod AI. On the distribution of residual autocorrelations in Box–Jenkins models. Journal of the Royal Statistical Society: Series B (Methodological). 1978; 40(3): 296-302.
Google Scholar
41
-
Horowitz JL, Lobato IN, Nankervis JC, Savin NE. Bootstrapping the Box–Pierce Q test: a robust test of uncorrelatedness. Journal of Econometrics. 2006; 133(2): 841-62.
Google Scholar
42
-
Fisher JD, Liu CT, Zhou R. When can we forecast inflation?. Economic Perspectives-Federal Reserve Bank of Chicago. 2002; 26(1): 32-44.
Google Scholar
43
-
Durevall MD, Ndung'u NS. A dynamic model of inflation for Kenya, 1974–1996. International Monetary Fund, 1999.
Google Scholar
44
-
Andrle M, Berg MA, Morales MR, Portillo R, Vlcek MJ. Forecasting and monetary policy analysis in low-income countries: Food and non-food inflation in Kenya. International Monetary Fund. 2013.
Google Scholar
45
-
Nyoni T. Modeling and forecasting inflation in Kenya: Recent insights from ARIMA and GARCH analysis. Dimorian Review. 2018; 5(6): 16-40.
Google Scholar
46
-
Filder TN, Muraya MM, Mutwiri RM. Application of seasonal autoregressive moving average models to analysis and forecasting of time series monthly rainfall patterns in Embu County, Kenya. Asian Journal of Probability and Statistics. 2019: 1-15.
Google Scholar
47
-
Kinene A. FORECASTING OF THE INFLATION RATES IN UGANDA: A COMPARISON OF ARIMA, SARIMA AND VECM MODELS. 2016.
Google Scholar
48
-
Uwilingiyimana C, Munga’tu J, Harerimana JD. Forecasting Inflation in Kenya Using Arima-Garch Models. International Journal of Management and Commerce Innovations. 2015; 3(2): 15-27.
Google Scholar
49
Most read articles by the same author(s)
-
Teddy Mutugi Wanjuki,
Victor Wandera Lumumba,
Emmanuel Koech Kimtai,
Morris Kateeti Mbaluka,
Elizabeth Wambui Njoroge,
Comparative Analysis of GARCH-Based Volatility Models of Financial Market Volatility: A Case of Nairobi Security Market PLC, Kenya , European Journal of Mathematics and Statistics: Vol. 5 No. 4 (2024) -
Morris Kateeti Mbaluka,
Dennis K. Muriithi,
Gladys G. Njoroge,
Application of Principal Component Analysis and Hierarchical Regression Model on Kenya Macroeconomic Indicators , European Journal of Mathematics and Statistics: Vol. 3 No. 1 (2022) -
Teddy M. Wanjuki,
Adolphus Wagala,
Dennis K. Muriithi,
Evaluating the Predictive Ability of Seasonal Autoregressive Integrated Moving Average (SARIMA) Models using Food and Beverages Price Index in Kenya , European Journal of Mathematics and Statistics: Vol. 3 No. 2 (2022) -
Mburu S. Njoroge,
Gladys G. Njoroge,
Adolphus Wagala,
Vector Error Correction Model: Prediction of Bi-Directional Causality between Gross Domestic Product and Wage Growth in Kenya , European Journal of Mathematics and Statistics: Vol. 3 No. 4 (2022) -
Kimutai K. Emmanuel,
Adolphus Wagala,
Dennis K. Muriithi,
Singular Spectrum Analysis: An Application to Kenya’s Industrial Inputs Price Index , European Journal of Mathematics and Statistics: Vol. 3 No. 1 (2022) -
Eric M. Ndege,
Dennis K. Muriithi,
Adolphus Wagala,
Application of Asymmetric-GARCH Type Models to The Kenyan Exchange Rates , European Journal of Mathematics and Statistics: Vol. 4 No. 4 (2023)