skip to main content

MODELING THE CONTRIBUTION OF THE MANUFACTURING SECTOR TO THE GROSS DOMESTIC PRODUCT OF KENYA USING TIME SERIES ANALYSIS

*Maurice Wanyonyi orcid  -  Department of Mathematics and Statistics, University of Embu, Kenya, Kenya
Open Access Copyright (c) 2022 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
The manufacturing sector is considered a pivotal contributor to the growth of the economy around the globe. Kenya relies on the manufacturing sector to generate revenue and ultimately enhance the growth of the economy. Despite the key purpose played by these sectors in the economy, inflation rate has diversely affected their performance. The purpose of the study was to develop the Autoregressive Integrated Moving Average time series model to forecast the inflation rate in Kenya. The analysis utilized secondary data from the Kenya National Bureau of Statistics and the model was fitted to the data using R. The ARIMA  with the information criterion of 576.24 was identified as the best model. Based on the forecasting, it was established that there will be a slight shift in the inflation in the coming years. Therefore, the government should use wage and price control to fight inflation but put in place policies to prevent recession and job loss in the country. The government should also employ contractionary monetary policy to fight inflation by reducing the money supply in the economy through decreases bond prices and increased interest rates.  Implementation of these recommendations might assist in reducing the rate of inflation in the country.
Fulltext View|Download
Keywords: Gross Domestic Product; Autoregressive Integrated Moving Average model; inflation rate; manufacturing sector.

Article Metrics:

  1. Adelheid, H. (2004) ‘Manufacturing Location and Impacts of Road Transport Infrastructure: Empirical Evidence from Spain. Regional Science and Urban Economics. 34. 341-363. 10.1016/S0166-0462(03)00059-0. Using micro-level data and geographic information system (GIS) techniques’, Sustainability
  2. Antonov, A. (2016) ‘Economics and Political Economy’, Automating Analytics: Forecasting Time Series in Economics and Business, 3(2)
  3. Barr, C. et al. (2021) ‘Prediction of confirmed and death cases of Covid-19 in Chile through time series techniques : A comparative study’, medRxiv. doi: https://doi.org/10.1101/2020.12.31.20249085
  4. Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1970) Time series analysis: Forecasting and control: Fourth edition, Wily & sons Inc. doi: 10.1002/9781118619193
  5. Brockwell, P. J. and Davis, R. A. (2002) Introduction to Time Series and Forecasting , Second Edition Springer Texts in Statistics
  6. Dramani, J. B. and Frimpong, P. B. (2020) ‘The effect of crude oil price shocks on macroeconomic stability in Ghana’, OPEC Energy Review, 44(3), pp. 249–277. doi: 10.1111/opec.12182
  7. Dritsakis, N. and Klazoglou, P. (2019) ‘Time series analysis using ARIMA models: An approach to forecasting health’, International Econmics, 72(1), pp. 77–106
  8. Filatova, H. and Aiyedogbon, J. O. (2020) ‘Government debt forecasting based on the Arima model’, Public and Municipal Finance, 8(1). doi: 10.21511/pmf.08(1).2019.11
  9. KAM (2019) Manufacturing priority agenda. Closing the manufacturing gap through the Big 4 Agenda for shared prosperity. Available at: https://kam.co.ke/kam/wp-content/uploads/2019/02/KAM-Priority-Agenda-2019-Abridged-version.pdf
  10. KAM (2021) Manufacturing Priority Agenda 2021. Available at: https://kam.co.ke/wp-content/uploads/2021/02/2021-Manufacturing-Priority-Agenda.pdf
  11. Karanja, P. and Kennedy, O. (2016) ‘Influence Of Organizational Structure On Performance Of Large Manufacturing Firms In Kenya’, European Journal of Business Management, 2(11), pp. 15–29
  12. Karpak, B. and Topcu, I. (2010) ‘Small medium manufacturing enterprises in Turkey: An analytic network process framework for prioritizing factors affecting success’, International Journal of Production Economics., 125(1)
  13. Kinyili, M. and Wanyonyi, M. (2021) ‘General Letters in Mathematics ( GLM ) Forecasting of Covid-19 deaths in South Africa using the autoregressive integrated moving average time series model’, General Letters in Mathematics, 11(2), pp. 26–35. doi: 10.31559/glm2021.11.2.2
  14. Kiprotich, E., Gachunga, H. and Bonuke, R. (2018) ‘Influence of cost leadership procurement strategy on performance of manufacturing firm in kenya’, European Journal of Business and Strategic Management, 3(1), pp. 32–51
  15. Mathenge M., R. (2019) ‘Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model’, International Journal of Statistical Distributions and Applications, 5(3), p. 46. doi: 10.11648/j.ijsd.20190503.11
  16. Ministry of finance (2019) Reimagine the possible Budget
  17. Montgomery, D. C., Jennings, C. L. and Kulahci, M. (2015) ‘Time Series Analysis and Forecasting’, in Introductio to time series analysis and forecasting. Second Edi, pp. 1–671
  18. Montgomery, D., Jennings, C. and Kulahci, M. (2008) Introduction to Time Series Analysis and Forecasting. 5th Editio. Canada: John Wiley & Sons Inc
  19. Mwangi, N. (2019) INFLUENCE OF SUPPLY CHAIN OPTIMIZATION ON THE PERFORMANCE OF MANUFACTURING FIRMS IN KENYA ( Supply Chain Management ) JOMO KENYATTA UNIVERSITY OF
  20. Nwanneka Judith, M. and Hillary Chijindu, E. (2016) ‘International Journal of Economics and Financial Issues Dynamics of Inflation and Manufacturing Sector Performance in Nigeria: Analysis of Effect and Causality’, International Journal of Economics and Financial Issues, 6(4), pp. 1400–1406. Available at: http:%0Awww.econjournals.com
  21. Oketch, C. S. (2014) Supply Chain Performance and Performance of Manufacturing Pharmaceutical Firms in Kenya a Research Project Submitted in Partial Fulfilment of the Requirements for the Award of Master of Business Administration (Mba), School of Business the
  22. Oláh, J. et al. (2019) ‘The assessment of non-financial risk sources of SMES in the V4 countries and Serbia’, Sustainability (Switzerland), 11(17), pp. 1–19. doi: 10.3390/su11174806
  23. Otieno, A. P. (2015) ‘Factors Influencing ICT Adoption and Usage by Small and Medium Sized Enterprises: The Case of Nairobi Based SMEs’, pp. 1–99
  24. Republic of Kenya (2018) Integrated National Export Development and Promotion Strategy. Available at: http://www.trade.go.ke/sites/default/files/NEDPS_Main_File_0.pdf
  25. Undesa (2020) ‘Recovering better: economic and social challenges and opportunities’, Department of Economic and Social Affairs, United Nations, New York, pp. 1–182
  26. Wanyonyi, M. et al. (2021) ‘COVID-19 PREDICTION IN KENYA USING THE ARIMA MODEL’, International journal of electrical engineering and technology, 12(8), pp. 105–114
  27. Yang, F. et al. (2021) ‘Factors affecting the manufacturing industry transformation and upgrading: A case study of guangdong–hong kong–macao greater bay area’, International Journal of Environmental Research and Public Health, 18(13). doi: 10.3390/ijerph18137157
  28. Yong, L. (2018) ‘Demand for Manufacturing: Driving Inclusive and Sustainable Industrial Development’, in united Nations Industrial Development Organization, pp. 1–274

Last update:

No citation recorded.

Last update: 2024-05-16 20:40:36

No citation recorded.