Adane Tufa (PhD)Misgan Desale2024-03-292024-03-292022-06-15https://etd.aau.edu.et/handle/123456789/2623This thesis introduces a new stochastic frontier model called a normal-weighted exponential stochastic frontier model. We have derived a closed form log-likelihood function and JLMS inefficiency estimator of a normal-weighted exponential stochastic frontier model. In addition, we have derived the gradient and hessian matrix of a normal-weighted exponential stochastic frontier model. A Monte Carlo (MC) simulation is carried out to verify the correctness of the derivations, of a normal-weighted exponential stochastic frontier model, and to study the finite sample properties of maximum likelihood estimator. Our simulation result shows that a normal weighted exponential stochastic frontier model performs well compared to a normal-exponential stochastic frontier model. In our simulation result, it shows that as sample size increases the bias and standard errors decreases. Moreover, a real data application is performed, and it is about estimation of carbon efficiency of manufacturing firms in Africa. We have estimated an input requirement production function, using fuel consumption as dependent variable and output and other inputs as independent variables. Our estimated result shows that the estimates of coefficients are the same across models. However, there is differences in carbon efficiency estimates of manufacturing firms. Using a normal-half normal stochastic frontier model, a carbon efficiency of manufacturing firms in Africa gives an estimate ranging between 1.002344 (99.8984%) and 1.002362 (99.8976%). For a normal-exponential stochastic frontier model the range of carbon inefficiency estimates are between 1.074752 (97.5092%) and 1.090364 (96.3126%). Similarly, for a normal-weighted exponential stochastic frontier model the carbon inefficiency estimates are between 1.122895 (95.0907%) and 1.237519 (91.1602%). We have used the carbon efficiency estimates to rank African countries and Egypt is the most carbon efficient country in Africa. We have also run a multiple linear regression on carbon inefficiency estimates to see the determinants. In all three stochastic frontier models: top manager work experience, obstacle to access finance, firm size, export status, and foreign ownership are the key determinants.enThesis