Estimating the parameters of a Gompertz-type diffusion process by means of Simulated Annealing

This paper explores the application of the Simulated Annealing algorithm for the maximum likelihood estimation of the parameters of a Gompertz-type process. Firstly, the solution space is bounded using relevant information about the process provided by the sample data. Secondly, a proposal for improvement is made, namely the application of a second cycle of the algorithm, including a refinement factor. Finally, both the specifications for the application of the algorithm and the proposed improvement are validated through their application to simulated and real data.