Gretl 2020a windows 64bit. Consider the following output from mle estimation (pay attention to the Gradient values and the NA std errors): << --- FINAL VALUES: loglikelihood = -1241.43371416 (steplength = 6.5536e-012) Parameters:       1.3798      1.0173     0.96596 3.7418e-012 Gradients:        2.4007      6.7772     -5.3557    -0.89559 (norm 1.96e+000) Tolerance = 1.81899e-012 Function evaluations: 273 Evaluations of gradient: 39 Model 2: ML, using observations 1-1000 logl = check ? obsll :NA Standard errors based on Outer Products matrix                estimate     std. error   z    p-value   ---------------------------------------------------   const      1.37979            NA       NA     NA   X1         1.01730               NA       NA     NA   sv         0.965960           NA       NA     NA   su         3.74177e-012       NA       NA     NA Warning: norm of gradient = 1.96088 I have a question, and  a suspicion about what the answer might be. My question is: since the Std errors are based on the Outer Products matrix, why are they not computed/presented ? Never mind whether this is a successful estimation attempt -it is not. My question is "mechanical". I would understand "NA" if I was asking for std errors based on the inverted Hessian. But where is the problem in computing std errors based on the gradient? My suspicion: maybe gretl in all cases dutifully computes the full variance expression, [G= likelihood gradient], namely Variance = E(GG') - E(G)E(G)'  (using sample means), i.e. it does not a priori treat as given that E(G)=0. Then, if it so happens that E(G) >> 0 (as is my case), the full expression may become negative, in which case std errors are reported as NA since they are the square root of the variance. Somebody please? -- Alecos Papadopoulos PhD Athens University of Economics and Business web: alecospapadopoulos.wordpress.com/ skype:alecos.papadopoulos