The main purpose of this work is to discuss Stata’s capability to implement customized likelihood functions compared with Gauss’s. I compare these two high-level programming languages with built-in function libraries and graphic routines. Overall, Stata’s features seem best suited for analyzing specific models of decision-making processes and other microeconometric applications, while Gauss is ideal for analyzing a more ample range of statistical issues based on maximum likelihood estimation. I briefly discuss such modeling capabilities, emphasizing what is still needed and what might be refined.
