Re: [Gretl-users] Hausman Test

Lema, The short answer is not bother too much with Hausman tests. The long answer is: (1) Re-parameterize your model as a multilevel model; (2) Decompose your variables into 'fixed' (within) and 'random' (between) components; (3) Run it in R (through gretl, if you wish), and then; (4) Do a joint F test of the equality of coefficients. In R, it would look something like this: <R> library(car,lme4) # assuming you have these packages installed in your R mydata<-read.csv("/.../mydata.csv",header=T,sep=",",fill=T) attach(mydata) # R purists like to lecture you about not using -attach()-; for now, ignore them m_x1=rep(tapply(x1,groupvar,mean),tapply(x1,groupvar,length)) d_x1=x1-m_x1 m_x2=rep(tapply(x2,groupvar,mean),tapply(x2,groupvar,length)) d_x2=x2-m_x2 fit1<-lmer(y~(1|groupvar)+(1|timevar),REML=T) summary(fit1) fit2<-lmer(y~d_x1+m_x1+d_x2+m_x2+(1|groupvar)+(1|timevar),REML=T) summary(fit2) anova(fit1,fit2) linearHypothesis(fit2,c("d_x1=m_x1","d_x2=m_x2")) </R> (NB: -timevar- should be ordered by way of a time counter starting at 1 *within* each of your -groupvars-.) If you have run the above routine successfully, it means that you have now tested for the equality (or not) of your fixed and random effects, thus obviating the need for any Hausman tests. I hope that helps and good luck. C On 26 April 2018 at 03:05, 3jlema <3jlema(a)gmail.com&gt; wrote: -- Clive Nicholas "My colleagues in the social sciences talk a great deal about methodology. I prefer to call it style." -- Freeman J. Dyson