Actually I needed the examples and reference for structural equation modelling in longitudinal data with repeated predictors and repeated outcomes.
Ferrer and McArdle (2003) provide a good overview of SEM with repeated measures.
Ferrer, E., & McArdle, J. (2003). Alternative structural models for multivariate longitudinal data analysis. Structural Equation Modeling, 10(4), 493-524.
You might want to consider explicitly modeling the structure of your predictors in the time domain. Thus you arrive at a bivariate model of change allowing you to model different forms of correlations of change (e.g., is change in X predicting change in Y, or even is change in change predicting change in change) across both processes. SEM offers full flexibility to any such hypotheses. Christoph has pointed you in the right direction but there is much more literature by Ferrer, McArdle and colleagues to explore on this topic. A recent one is
The bivariate latent change score model for multiple occasions.
McArdle, John J.; Nesselroade, John R.
McArdle, John J. Nesselroade, John R. , (2014). Longitudinal data analysis using structural equation models. , (pp. 291-300). Washington, DC, US: American Psychological Association, xi, 426 pp. http://dx.doi.org/10.1037/14440-025
Thanks, for the hint! It seems to be a comprehensive and detailed book on panel SEM!
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