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Bayesian inference approach in modeling and forecasting maize production in Rwanda
Department of Mathematics, College of Science and Technology, University of Rwanda, Kigali, Rwanda, (RWA).
Department of Mathematics, College of Science and Technology, University of Rwanda, Kigali, Rwanda, (RWA).
2018 (English)In: African Journal of Applied Statistics, ISSN 2316-0861, Vol. 5, no 2, p. 503-517Article in journal (Refereed) Published
Abstract [en]

Rwanda is the country whose economy relies on agriculture. Therefore,forecast in agriculture sector is very important in Rwanda for future plan. In ourstudy, secondary annual data from the agricultural ministry (MINAGRI), spanningfrom 1960 to 2014 have been used. In the analysis, appropriate model is selectedbased on the appearance of ACF and PACF of the transformed data. In addition tothat, we use the fitted model to provide a four year forecasts of maize productionfrom 2015 to 2018. Through Box–Jenkins methodology, the appropriate model isARIMA (1,2,1) and fit the data at 91%. From the results and forecast, it is seen thatthe production of maize in Rwanda will have an increasing trend in the future. Tostrengthen the model, we also use the MCMC algorithm as an alternative methodin parameters estimation. Diagnostics prove the chains’ convergence which is thesign of an accurate model. 

Place, publisher, year, edition, pages
2018. Vol. 5, no 2, p. 503-517
Keywords [en]
maize, time series model, Box–Jenkins methodology, forecast, MCMC method
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:fhs:diva-12396DOI: 10.16929/ajas/503.227OAI: oai:DiVA.org:fhs-12396DiVA, id: diva2:1861241
Available from: 2024-05-27 Created: 2024-05-27 Last updated: 2024-06-03Bibliographically approved

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Uwamariya, Denise

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CiteExportLink to record
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Citation style
  • apa
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  • ieee
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  • Other style
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  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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