This study examined the relationship between exchange rate and economic growth in Nigeria between 1981 and 2020. The specific objectives are to determine the effects of exchange rate, inflation and interest rate on gross domestic product (GDP). The data on the variables were obtained from the Central Bank of Nigeria (CBN) Statistical Bulletin and World Development Indicators, and analyzed using descriptive statistics, unit root as well as bounds cointegration tests and ARDL model. The unit root test results showed that the variables are mixed integrated. While inflation is stationary at levels, the other variables in the model were stationary at first difference. The bounds cointegration test showed that long run relationship exists between GDP growth and the underlying explanatory variables. The findings showed that exchange rate and inflation negatively impacted on economic growth. This finding indicates that increase in exchange rate and price level is detrimental to the growth of the Nigerian economy. There is evidence of a significant positive effect of interest rate on GDP growth. This finding explains the reality in Nigeria, where businesses and households tend to borrow even as interest rate increases, but tend to cut corners by reducing the quality of their products and services or pass-on the increased costs of borrowing to consumers by increasing prices. Given the findings, this study recommends amongst others that the federal government through the CBN should ensure that exchange rate policy should is consistent to provide opportunity for a realistic and stable exchange rate capable of driving economic growth in Nigeria.
Oil Resource Abundance and Agricultural Productivity in Nigeria: An Autoregressive Distributed Lag Approach (Published)
This paper analyzed and estimated the impact of oil abundance on agricultural productivity in Nigeria for the sample period of 1980 – 2018. The Autoregressive Distributed Lag model (ARDL) estimated with the Ordinary Least Square technique was used to examine the relationship among the variables. Findings from the model revealed that there was a negative and significant relationship between oil abundance and agricultural productivity in the short run while a negative and insignificant relationship existed in the long run. There was a direct and insignificant relationship between growth rate of GDP and agricultural productivity. The study therefore recommended subsidizing agricultural inputs and setting in place incentives that will keep people in the agricultural sector.
This article is aimed at providing empirical evidence on the impact of human capital development on industrial growth in Nigeria. Time series data spanning 1976-2016 period on relevant variables were analyzed using both descriptive and econometric techniques. ADF procedures were used to test for stationarity of the variables. The results show that the variables moved towards equilibrium in the long-run. The results also show that recurrent expenditure on education and health has a negative impact on industrial growth. The goodness of fit was encouraging. This article asserts that rigorous pursuance of graduate skill acquisition programmes as well as adherence to the 26 per cent minimum budgetary allocation demanded by UNESCO for education which will spur improvement in human capital development will impact industrial growth positively. More-so, incentives such as tax holidays, pioneer reliefs and exemptions that aids increased investment in industrial growth be vigorously pursued by governments at all levels in Nigeria.
Most time series analysts have used different technical and fundamental approach in modeling and to forecast exchange rate in both develop and developing countries, whereas the forecast result varies base on the approach used or applied. In these view, a time domain model (fundamental approach) makes the use of Box Jenkins approach was applied to a developing country like Nigeria to forecast the naira/dollar exchange rate for the period January 1994 to December 2011 using ARIMA model. The result reveals that there is an upward trend and the 2nd difference of the series was stationary, meaning that the series was I (2). Base on the selection criteria AIC and BIC, the best model that explains the series was found to be ARIMA (1, 2, 1). The diagnosis on such model was confirmed, the error was white noise, presence of no serial correlation and a forecast for period of 12 months terms was made which indicates that the naira will continue to depreciate with these forecasted time period.