Effect of Foreign Direct Investment on Exchange Rate of Naira: A Multi-Sectoral Analysis (Published)
This study examines the effect of foreign direct investment on exchange rate of naira. It covers the period between 1990 and 2016. The unusual depreciation of the naira accompanied by the declining trend of foreign direct investment inflows among other things necessitated this study. Ordinary Least Square Regression Analysis was used to estimate the model relationships. It made use of time series secondary data with five explanatory variables (FDI inflows to Agriculture, forestry and fishery, building and construction, manufacturing and processing, mining and quarrying and transport and communication) and one dependent variable (Exchange Rate). The data were sourced from Central Bank of Nigeria (CBN) statistical bulletin, World Bank Data and Journal Articles. Tests that were carried out include Unit Root Test, Co-integration test and Granger Causality test. The study reveals that there is a positive significant effect of FDI inflow to building and construction on real exchange rate; there is a positive significant effect of FDI inflows to mining and quarrying on real exchange rate and there is a positive significant effect of FDI inflows to transport and communication on real exchange rate. However, there is an universe effect of FDI inflows to agriculture, forestry, fishery on real exchange rate and an inverse effect of FDI inflows to manufacturing and processing on real exchange rate. Based on these findings, the study recommends: massive investment of local investors in the agricultural and manufacturing sectors to strengthen the exchange rate of naira and also serious efforts to increase foreign direct investment inflows in the building, mining and transport sectors in Nigeria be sustained and improved upon to have a strong exchange rate of naira.
This paper is on SARIMA modelling of monthly Naira-CFA Franc exchange rates. The time plot of the realisation from January 2004 to June 2013 in Figure 1 shows an overall upward secular trend with no clear seasonality. The time plot of the seasonal (i.e. 12-monthly) differences in Figure 2 shows an overall horizontal trend with no definite seasonality. The time plot of further non-seasonal differences in Figure 3 shows a horizontal trend and still no clear seasonality. The autocorrelation function of the resultant series of Figure 4 has a significant negative spike at lag 12 indicating a 12-monthly seasonality and the involvement of a seasonal moving average component of order one. Its partial autocorrelation function has a significant spike at lag 12 suggesting the inclusion of a seasonal autoregressive component of order one. Using the duality relationship between autoregressive and moving average models, it is argued that this autoregressive component of high order (i.e. of order 12) be replaced by a moving average component of (low) order one. Hence an additive SARIMA model with significant lags 1 and 12 is proposed and fitted. The model is shown to be adequate