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Why are mean variables included in a CRE model?
To allow the individual effect to be correlated with the explanatory variables.
Why do time-invariant variables not appear as means in CRE?
For a time-invariant variable X-i = Xi. Including both creates perfect multicollinearity.
What are the hypotheses for the CRE test?
H0: B(j) = 0 ; H1: at least one B(j) not 0
Distribution in CRE test?
Chissq_q, where q is the number of restrictions
CRE test decision rule?
At the 5% significance level, reject H0 if Chissq > Chissq(0.95,q)
CRE test conclusion if χ2>χc2
We reject H0. There is sufficient evidence that the mean variables are jointly significant. Therefore, the Random Effects estimator is not appropriate, and the Correlated Random Effects model is preferred.
CRE test conclusion if χ2<χc
We fail to reject H0. There is insufficient evidence that the mean variables are jointly significant and the sample evidence is consistent with Random Effects estimator being appropriate