plm

I am trying to estimate a panel dataset with an interaction term for geographical areas (LoadArea, DischargeArea) which signifies a route. Using the fixed effects specification, it does not like the interaction term (LoadArea * DischargeArea) and produces the following error when you summarize the regression: mult_fe<-plm(log(DayRate)~LoadArea *DischargeArea + factor(Laycan.Day.Diff) + CapUtil + Age + I(Age^2) + WFRDWT + lag_BDTI, data=mult_reg1,model="within"); summary(mult_fe) Error in crossprod(t(X), beta) : non-conformable arguments This works fine in a normal OLS regression replacing plm

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After reading this answer on error in contrasts and reviewing my data I am still stuck with a problem when trying to combine packages 'plm' and 'effects'. This might be impossible as John Fox does not discuss this possibility in his effects document (link is not allowed apparently - Google: "john fox effects package" if you want to have a look). So if it is indeed impossible please tell me. I am running a simple regression on a reduced data set library(plm) ; library(effects) shortdata<-plm.data(shortdata,index=c("ID","Year")) MESS<-plm(paci_to_t ~ paco_to_t + cddom + cddom2,data=shortdata

I have a panel data set in R (time and cross section) and would like to compute standard errors that are clustered by two dimensions, because my residuals are correlated both ways. Googling around I found http://thetarzan.wordpress.com/2011/06/11/clustered-standard-errors-in-r/ which provides a function to do this. It seems a bit ad-hoc so I wanted to know if there is a package that has been tested and does this? I know sandwich does HAC standard errors, but it doesn't do double clustering (i.e. along two dimensions). Frank Harrell's package rms (which used to be named Design ) has a function

I have a small N large T panel which I am estimating via plm (panel linear regression model), with fixed effects. Is there any way to get predicted values for a new dataset? (I want to estimate parameters on a subset of my sample, and then use these to calculate model-implied values for the whole sample). Thanks! There are (at least) two methods in the package to produce estimates from plm objects: -- fixef.plm: Extract the Fixed Effects -- pmodel.response: A function to extract the model.response It appears to me that the author(s) are not interested in providing estimates for the "random