Another query about mle. As before, I have been experimenting with stochastic frontier models and I have found that mle behaves entirely differently with two formulations that seem to be equivalent. Version 1: mle logl=ln(cnorm(e*lambda/ss))-(ln(ss)+0.5*(e/ss)^2) scalar ss = sqrt(su^2 + sv^2) scalar lambda=su/sv series e=y-lincomb(xlist,b) params b su sv end mle --hessian --verbose Version 2 mle logl=llnow scalar ss = sqrt(su^2 + sv^2) scalar lambda=su/sv series e=y-lincomb(xlist,b) series llnow=ln(cnorm(e*lambda/ss))-(ln(ss)+0.5*(e/ss)^2) params b su sv end mle --hessian --verbose Both report identical log-likelihoods & gradients at iteration 1, but version 2 blows up at iteration 2 while version 1 continues properly. Technically, the reason for the blow-up in version 2 is that it uses a step length of 1, whereas version 1 uses a step length of 1e-7. I cannot understand the reason for the different behaviour but in this case it is critical. Gordon Hughes