Dear all, I have a basic(!) econometric question which i couldnt find the way to do it in R. Well this could be also because of my wrong interpretation of the econometric process that i am trying to implemet.so here i wanna ask if am doing a logical mistake!!!
so here is the question with the explanation of the process, hope there will be someone who can help me! suppose i have a basic Cobb-Douglas production function, ( i am not gonna give many information about the R commands or about the data since my questionn is rather theoric) and i run this model with OLS as following; >mdl1 = lm(lnQ~lnC+lnL+lnM+lnE,data=newdata) than in the second step, i need to get the predicted residual as a mesure of "total factor productivity" ==> epsilon(hat)it= lnQit-lnQ(hat)it and i get the residual by typing; residuals(mdl1) ==> do i make mistake here or should i write another command get the epsilon(hat), suppose i typed the correct command than i save the residuals as a vector with >resid<-mdl1$residuals supposing that epsilon(hat) measures the TFP (TFPit=epsilon(it)), i need to regress knowledge variables on it, such as "eco" and "RD" and i want to use panel-data techniques in order to get the effect of eco and RD net of unobserved heterogeneity. so the theortically the model that i want to estimate is TFPit=alpha0+alpha1(eco)+alpha2(RD)+Ui+Vt+Wit so i run this command; >mdl2<-plm(resid~eco+RD, data=newdata) then there is an error Error in model.frame.default(formula = resid ~ eco + RD, data = ds, drop.unused.levels = TRUE) : variable lengths differ (found for 'eco') well here in fact, probably i am doing a econometrical mistake that might be quite easy for somepeople, think that it is a silly question, but i need help in this situation... if someone knows about Total Factor Productivity and willing to help i would be greatful cheers [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.