I am trying to fit a model including three simple latent change regressions with covarying predictors and two latent change scores having direct effects onto the third latent change score. Unfortunately, the optimizer ends up with a non-positive sd covariance matrix. Some of the predictors are rather highly correlated (r ~ .8) and I was thinking wether this failure might be due to predictor multicollinearity. However, Mplus finds a solution! I was wondering if there are some default settings making the OpenMx model different from the Mplus model but I didn't figure it out. Setting the covariances to zero makes the two models (OpenMx and Mplus) look completely the same.
Atached please find the code.