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• Comparing ols and rank-based estimation techniques for production analysis: An application to Ghanaian maize farms.

  125-130

  Henry De-Graft Acquah

  Views:

  148

  This paper introduces the rank-based estimation method to modelling the Cobb-Douglas production function as an alternative to the least squares approach. The intent is to demonstrate how a nonparametric regression based on a rank-based estimator can be used to estimate a Cobb-Douglas production function using data on maize production from Ghana. The nonparametric results are compared to common parametric specification using the ordinary least squares regression. Results of the study indicate that the estimated coefficients of the CobbDouglas Model using the Least squares method and the rank-based regression analysis are similar. Findings indicated that in both estimation techniques, land and Equipment had a significant and positive influence on output whilst agrochemicals had a significantly negative effect on output. Additionally, seeds which also had a negative influence on output was found to be significant in the robust rank-based estimation, but insignificant in the ordinary least square estimation. Both the least squares and rank-based regression suggest that the farmers were operating at an increasing returns to scale. In effect this paper demonstrate the usefulness of the rank-based estimation in production analysis.

  JEL CODE: Q18, D24, Q12, C1 and C67

• Comparing parametric and semiparametric error correction models for estimation of long run equilibrium between exports and imports

  19-23

  Henry De-Graft Acquah

  Lawrence Acheampong

  Views:

  167

  This paper introduces the semiparametric error correction model for estimation of export-import relationship as an alternative to the least squares approach. The intent is to demonstrate how semiparametric error correction model can be used to estimate the relationship between Ghana’s export and import within the context of a generalized additive modelling (GAM) framework. The semiparametric results are compared to common parametric specification using the ordinary least squares regression. The results from the semiparametric and parametric error correction models (ECM) indicate that the error correction term and import variable are significant determinants of Ghana’s exports. On the basis of Akaike Information Criteria and Generalized Cross-Validation (GCV) scores, it is found that the semiparametric error correction model provides a better fit than the widely used parametric error correction model for modeling Ghana’s export-import relationship. The results of the analysis of variance provide further evidence of nonlinearity in Ghana’s export and import relationship. In effect, this paper demonstrates the usefulness of semiparametric error correction model in the estimation of export – import relationship.

  JEL code: C14, C18, C22, F10, F14

• Sensitivity of technical efficiency estimates to estimation methods: an empirical comparison of parametric and non-parametric approaches

  67-72

  Henry de-Graft Acquah

  Views:

  113

  This paper highlights the sensitivity of technical efficiency estimates to estimation approaches using empirical data. Firm specific technical efficiency and mean technical efficiency are estimated using the non parametric Data Envelope Analysis (DEA) and the parametric Corrected Ordinary Least Squares (COLS) and Stochastic Frontier Analysis (SFA) approaches. Mean technical efficiency is found to be sensitive to the choice of estimation technique. Analysis of variance and Tukey’s test suggests significant differences in means between efficiency scores from different methods. In general the DEA and SFA frontiers resulted in higher mean technical efficiency estimates than the COLS production frontier. The efficiency estimates of the DEA have the smallest variability when compared with the SFA and COLS. There exists a strong positive correlation between the efficiency estimates based on the three methods.