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Relying on the mean approach in the estimation of the Cobb-Douglas production function may cloud the empirical linkage between farm specific inputs and outputs in production analysis. In order to address the issue, this paper introduces the quantile regression estimation method to modelling the Cobb-Douglas production function as an alternative to the least squares approach in maize production analysis. The quantile regression results are compared to the widely used ordinary least squares regression. Results of the OLS estimation find an insignificant linkage between labour and maize output as well as fertilizer and maize output. However, the quantile regression estimation reveals a significant positive link between labour and maize output as well as fertilizer and maize output. OLS underestimate and overestimate the true relationship between inputs and output depending on the position of the quantile. These results suggest that the quantile regression methods might provide a more suitable framework for exploring the input output relationship in production analysis than the least squares method when the coefficient of inputs varies significantly across the distribution of farm output.

COMPARING OLS AND QUANTILE REGRESSION ESTIMATION TECHNIQUES FOR PRODUCTION ANALYSIS: AN APPLICATION TO GHANAIAN MAIZE FARMS