Multivariate Time Series Analysis on the Prices of Staple Foodstuffs in Kwara State, Nigeria

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dc.contributor.authorAfolayan, Razaq
dc.contributor.authorYahya, Waheed
dc.contributor.authorGarba, Mohammed
dc.contributor.authorAdenuga, A. A.
dc.contributor.authorOlatayo, T.
dc.date.accessioned2021-10-12T11:42:49Z
dc.date.available2021-10-12T11:42:49Z
dc.date.issued2016
dc.description.abstractDue to supplementary, complementary and substitute relationship between staple foodstuffs, the prices of one or more staple foodstuffs tend to influence and could be used to predict the prices of some others. This study was therefore aimed at establishing the co-movement between the prices of some major stable foodstuffs - Rice, Maize, Garri, Millet, Guinea-Corn and Beans - in Kwara State, Nigeria. Multivariate time series models were fitted to data on monthly prices of Rice, Maize, Garri, Millet, Guinea-Corn and Beans over a period of twelve years (from January 2000 to December 2012). The cointegration relations among the prices were established by applying Johansen’s cointegration tests. As a result, appropriate Vector Error Correction (VEC) model was fitted to the data. The unit root test for stationarity in the series reveals that all the series were non-stationary but they were only made to be stationary at first difference. The results from the analysis showed that there exist short term adjustments and long-term dynamics among the prices of Rice, Maize, Garri, Millet, Guinea-Corn and Beans in Nigeria over the study period. Further results showed that a Vector Error Correction (VEC) model of lag two with one cointegration equation best fits the data. The forecasting accuracy of the fitted model was determined by out-of-sample forecasts of the future prices of the selected staple foodstuffs. Suitable model’s assessment criteria such as root mean square error, mean absolute error and the like were employed to determine the efficiency of the fitted model. The data employed for the study were collected from the Kwara State office of National Bureau of Statistics, Nigeria. All analyses were performed in the environment of R Statistical package.en_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/6593
dc.language.isoenen_US
dc.publisherDepartment of Science Education, Federal University of Technology Minna.en_US
dc.relation.ispartofseriesVol. 12(3);107-123
dc.subjectVector autoregressiveen_US
dc.subjectco-integrationen_US
dc.subjectVector Error correction model and forecastingen_US
dc.subjectPricesen_US
dc.titleMultivariate Time Series Analysis on the Prices of Staple Foodstuffs in Kwara State, Nigeriaen_US
dc.typeArticleen_US

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