Panel data-RE

Question 1

Difference between FE and RE?

Answer 1

FE-assume ui is correlated with xit

RE-ui is uncorrelated with xit

Question 2

Model for RE?

Answer 2

yit=B0+B1xit+ui+eit
where vit=ui+eit
ui=between group error common to all observations within group (not indexed by t_
eit=within group error

Question 3

what is ui?

Answer 3

bewteen-group error: common to all observations within group, not indexed by t

Question 4

what is eit?

Answer 4

within-group error

Question 5

True or false: eit and ui are distributed normally and iid

Answer 5

True

Question 6

Is vit (composed of ui and eit) correlated with xit?

Answer 6

no, which means b1 will be unbiased and consistent

Question 7

True or false: the composite error term vit is iid

Answer 7

False–The common error for observations in group i (ui ) results in correlation
between the composite error in period t (vit) and in period s (vis).

Question 8

vit is not iid–what does this mean for OLS consistency and efficiency?

Answer 8

OLS is consistent but not efficient, and that traditional standard error formulas assuming i.i.d. errors are incorrect.

Question 9

formula for intra-class correlation?

Answer 9

(sigma squared u)/(signa squared u+signa squared e)=

between group error variance/between group error variance+within group error variance

Question 10

If FE assumptions hold (correlation between xit and ui), what does this mean for bias and consistency of RE model?

Answer 10

biased and inconsisent

Question 11

If RE assumpttions hold (no correlation between xit and ui), what does this mean for consistency of RE and FE?

Answer 11

they are both consisent

Question 12

If RE assumptions hold (no correlation between xit and ui), what does this mean for efficiency of FE?

Answer 12

FE model will be inefficient

Question 13

If you get different answers from FE and RE, what does this mean?

Answer 13

you should use FE

Question 14

How do you test if you should use FE or RE?

Answer 14

Hausman test

Question 15

null and alternative hypotheses for Hausman test?

Answer 15

Null hypothesis: RE assumptions hold, both estimators consistent but RE is efficient.

Alternative: RE assumptions do not hold and the RE estimator is inconsistent

Question 16

GLS transformation where var(u)=k σ^2u-how do you weight?

Answer 16

The GLS transformation divides the data by

Observations with greater variance get less weight.

Question 17

Should you use RE or FE here: assignment to treatment was random at the school level, so we need not be concerned about correlation between treatment and the error term. However, the errors are not i.i.d.

Answer 17

RE