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Exchange Rate, Interest Rate and Agricultural Export Earnings: An analysis Using Panel Data Vector Autoregressive Model

The study modeled the dynamic interaction between exchange rate, interest rate and agricultural export earnings using panel VAR Model. The specific objectives of the study include to; interdependencies in the dynamic interaction between exchange rate, interest rate and agricultural export earnings, parameters of panel VAR model using PVAR Stata code developed by Abrigo and love, determine the shocks associated with their dynamic interactions between these variables, investigate direction of causality between interest rate, exchange rate and agricultural export earnings from six African countries and make appropriate recommendations. The data used for the study was secondary data extracted from index mundi website and world data indicators for the period of 40 years (1980-2020). The data was on exchange rate, interest rate and agricultural export earnings. Geographically, the six African countries include; Algeria, Angola, Egypt, Libya, Gabon and Nigeria. The study uses vector Autoregressive model estimation results with PVAR Stata code developed by Abrigo and love. The post estimation test on the Vector Autoregressive (VAR) model shows a contemporary Co-efficient of Correlation analysis. It was found that lending interest rate and exchange rate are negatively associated with Co-efficient of Correlation of (-0.0873). Also, it was found that there exist a positive association between exchange rate and agricultural export earnings. Also, there is a positive association between lending interest rate and agricultural export earnings. The inverse roots of a characteristic polynomial of the estimated Panel VAR model satisfied the stability condition (of the diagnostic test) since no root lied outside the unit root circle. Therefore, the estimated VAR is stable. However, it was confirmed that there is no directional relationship that exist between the variables. Also, the results show that exchange rate and lending rate have positive on agricultural export earnings, whereas exchange rate is likely to reduce the level of lending interest rate slightly. Therefore, it is recommended that in estimating the dynamic interaction between variables in a panel data system, there is need for the inclusion of the lags of the response variable among the determinants to measures the dynamic interaction as well capture heterogeneities in the series and also, policies should be formulated to stabilized exchange and lending rates in order to improve and strengthen the countries’ agricultural economy amongst others