IIARD International Journal of Economics and Business Management (IJEBM )
E-ISSN 2489-0065
P-ISSN 2695-186X
VOL. 2 NO. 3 2016
Eze, Titus Chinweuba (Ph.D)
This paper is aimed at examining the long run impact of Agricultural output to the growth of industrial sector in Nigeria between. The methodology adopted for the study is multiple regression analysis employing vector error correction model (VECM) technique, complemented with paire-wise granger causality test to confirm if there is any feedback effect among the series used. The variables included in the model are: Industrial output (INDST) as the dependent variable, and Agricultural output (AGO); exchange rate (EXR) and price of manufacturing output (PMANU) as the explanatory variables. Using time series data spanning from 1970 to 2014, the result of the regression analysis show that: (i) there exist a significant but negative long run relationship between agricultural output and the growth of industrial sector in Nigeria; (ii) an inverse and significant link exists between exchange rate and the growth of industrial sector in Nigeria; (iii) changes in the prices of manufacturing output positively and significantly predicts the growth of industrial sector in Nigeria, and finally, (iv) there is no feedback effect between Agricultural output and industrial sector in Nigeria. Based on the empirical findings, the author proffers the following suggestions: (i) government should encourage the production of more agricultural products that could be used as raw materials by industrial sector in order to achieve balanced growth; (ii) Central bank of Nigeria should vigorously continue to pursue policies that could maintain stable and low exchange rate regime; (iii) finally, Effort should be intensified in providing soft loans for the growth and development of small and medium scale industries (SMEs) to the young school leavers in the country by the three tiers of government
Granger Causality, Vector error correction model, Dependent variable, Explanatory variable, Regression, Predict, Exchange rate, Time series, Raw material.
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