Author
Listed:
- Joel Chiadikobi Nwaubani
(Advance Higher Education London, UK)
- Adanma Ngozi Ohia
(University of Portharcourt, Nigeria)
- Christiana Uzoma Ezechukwu
(Agricultural Development Programme (ADP), Nigeria)
- Peace Opara
(Abia Secondary Education Management Board (SEMB), Nigeria)
- Chinwe Adaugo Uzokwe
(Michael Okpara University of Agriculture Umudike, Nigeria)
- Uchechi Mgbafor Ezeji
(Amuzukwu Girls’ Secondary School, Nigeria)
Abstract
The structure of the European Union producer price indices, nominal total agricultural production varies from one country to another. The EU agricultural price indices involve the index of producer prices of agricultural products and the index of purchase prices of the means of agricultural production. The purpose of agricultural price indices is to unveil trends in the prices of individual agricultural products and purchase prices of the means of agricultural production. Moreover, the objective of the applying statistics on agricultural prices is to make comparisons between member states and also for economic analyses. Absolute agricultural prices are needed for many model calculations and for the ascertainment of price elasticity. The means through which these objectives could be achieved are believed to be when the absolute prices are compared between the member states, and also, when the products for which the prices from the respective member state are to be recorded for economic relevance. These objectives are not always compatible and sometimes require some compromise. In this study, we evaluate the price indices of domestic agricultural production as a whole in the EU24, using the most accurate association model of the Categorical Data Analysis. Figures from the Eurostat office calculated on annual base year from 2005-2017 were used to analyse this study. Since the main focus is to have a better understanding of producer price indices, nominal total domestic agricultural production, the analysis of association table (ANOAS) is given in order to ascertain the percentage of the data which is covered by each model. We find and estimate the association model with the best fit and in conclusion we find out that the Row-Effects Association Model (R) has the best fit because it covers 93% of the data, thereby giving the best fit among all.
Suggested Citation
Joel Chiadikobi Nwaubani & Adanma Ngozi Ohia & Christiana Uzoma Ezechukwu & Peace Opara & Chinwe Adaugo Uzokwe & Uchechi Mgbafor Ezeji, 2020.
"Finding the Appropriate Model to Evaluate the Price Indices of Domestic Agricultural Production in EU24,"
European Journal of Business and Management Research, European Open Science, vol. 5(6), November.
Handle:
RePEc:epw:ejbmr0:v:5:y:2020:i:6:id:50556
DOI: 10.24018/ejbmr.2020.5.6.556
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