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Hi, I am running a VERY simple path model with repeated measures over time (math scores at T1, T2, T3). I want earlier math scores to predict later math scores but I also want to account for the autoregressive nature of it. I was under the impression that I cannot have the same variables in an ON statement and a WITH statement. Therefore, how do I account for the correlation of the errors between Y1, Y2, and Y3 in the following model? Model: Y3 ON Y2 Y1; Y2 ON Y1; Y1 ON X1X5; Thanks so much! 


You can add y3 with y2; etc because that refers to the residuals of y3 and y2 given that they are dependent variables. But, your model will not be identified if you both regress y3 on y2 and let their residuals correlate. 


Right, that is what I was afraid of. So what is the right way to specify this model that had these lagged effects without getting biased coeficients? Or am I totally not understanding? Thanks! 


You have to limit yourself to the model y2 on y1; y3 on y2; and hope any residual correlation is small. To also identify correlated residuals you have to have multiple indicators at each timepoint  see the Wheaton et al (1977) article in the Soc Meth book. 


Hi Dr Muthen, I'm doing a path model with 2 forms of aggression (Y1 and Y2) on 8 independent variables (X1X8). I need to control for the high correlation between Y1 and Y2. Will the command "Y1 with Y2;" control for shared variance or will it only report error correlation ? I also tried Y1 on Y2; Y2 on Y1; But I have a justidentified model. My question : How can I control for the shared variance between Y1 and Y2 ? Y1 on X1X8; Y2 on X1X8; Y1 with Y2; Thank you 


The statement y1 WITH y2 estimates a residual covariance. I'm not sure what you mean about the shared variance. 

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