Parameter Estimation of Cobb Douglas Production Function with Multiplicative and Additive Errors using the Frequentist and Bayesian Approaches

摘要

Nonlinear Models are generally classified as intrinsically nonlinear and intrinsically linear based on the specification of the errors. This study was aimed at estimating the parameters of Cobb-Douglas production function with additive and multiplicative errors using the classical and Bayesian approaches. The classical nonlinear method considered is the Gauss-Newton iterative Method while the Bayesian estimation was carried out using the Metropolis-within-Gibbs with independent normal-Gamma prior. For the classical, the results showed that the estimates of the parameters of the Cobb-Douglas function with additive errors performed better than those for the multiplicative errors. However, similar estimates were obtained for both multiplicative and additive errors for the Bayesian approach. Overall, the Bayesian method performed better than the classical approach.