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Is there a way to plot residual autocorrelation (ACF, PACF) of Y in n=1 timeseries analysis? 


Autocorrelation plots are available. I don't know what you mean by residual autocorr plots. 


If I fit a n=1 timeseries model with covariates shown below I can plot the raw autocorrelation function of Y. However, is there a way to plot the autocorrelation function of the residuals of Y? Y ON X1 X2 X3; I red about RDSEM and tried the following code to model e.g. AR(2) model. Y ON X1 X2 X3; Y^ ON Y^1 Y^2; But there is no way to obtain ACF of Y^ in order to have a clue on the autocorrelation pattern needed? 


I would recommend to keep increasing the lag until the new autocorrelation is no longer significant. For example, if you estimate Y ON X1 X2 X3; Y^ ON Y^1 Y^2 Y^3; and the coefficient in front of Y^3 is no longer significant I would conclude that lag 2 is enough. You can go a bit further to make sure that something cyclical is not happening. 


Thanks! 


Is it possible to define residual autocorrelation structure for betweenlevel response variable? I tried but got the warning below: "Variables specified on the BETWEEN option cannot have lag and be mentioned on the LAGGED option." 


What does the Between level portray? Why is time on Between? 


Yes, I know the setup is a bit awkward. I have a response variable that has been measured annually (i.e., N=1 timeseries) for some 80 years (=betweenlevel) and I'd like to know how some IVs measured several times within years (=withinlevel) influence the response's variation at the betweenlevel. Can such a model be fit? I guess it would be a bit like adding a spatial autocorrelation into the model? 


Maybe you could try the autoregressive approach of UG ex6.17 and somehow apply it to the Between level. 

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