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Flashcards in Panel data Deck (31)
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what is panel data

a panel data set contains repeated observations over time for individuals, firms, countries etc


what is heterogeneity

the quality or state of being diverse in character or content


what is the main advantage of panel data

we are able to allow for certain forms of unobserved individual heterogeneity that is constant over time which cannot be done with cross-sectional or time-series


what is the panel data equation

yit = xit'β + vit,
=xit'β + αi + uit,
α are unobserved constant individual effects,
uit idiosyncratic shock


how does Pooled OLS and Random Effects GLS deal with panel data

Pooled OLS and Random Effects GLS are biased and inconsistent if the unobserved fixed individual components αi are correlated with the explanatory variables xit


why are Fixed-Effects OLS and First-Differenced OLS better with panel data

they are also consistent when there is correlation between the αi and the xit.
They solve the endogeneity without need for instrumental variables


what does the pooled OLS do

simply treats the panel as a very large cross-section with nT observations


what are the four estimators for panel data

pooled OLS,
random effects GLS,
Fixed effects OLS,
First differenced OLS


what is the main condition for pooled OLS

this means E(α|Xi)=0 and E(uit|xit)=0,
for pooled to be consistent there cannot be correlation between the unobserved fixed component αi and xit


for pooled OLS with vit=αi+uit what is the variance

E(vitvis)≠0 for s≠t,
if E(uituis)=0 for s≠t then,
E(vitvis)=E(αi^2)=σσ^2 (second σ is subscript),
standard errors that don't take the serial correlation of vit into account will be wrong (try derive from E(vitvis) in notes)


do you need cluster robust standard errors for pooled OLS

yes, standard errors don't take the serial correlation of vit into account will be wrong,
need cluster rob se unless σσ^2=0 (2nd σ subscript)


what is the main assumption of the random effects GLS

E(αi|Xi)=0 and xit strictly exogenous,


how is the random effects GLS better than pooled OLS

improves efficiency of pooled ols by taking clusters into account


what is process of random effects gls

takes stylised model, assumes complete homoskedasticity, get a variance covariance matrix and plug into GLS estimator (like WLS in that it divides)


what are the assumptions for the consistency and efficiency of random effects GLS (equations)



what is the case in a random effects GLS when var=Ω

when var=Ω the estimator is efficient


what is the case in random effects GLS when var≠Ω

if not, as long as strict exogeneity the estimator is still consistent and normal but no longer efficient


what are the assumptions in words for the efficiency of random effects GLS

strict exogeneity,
no correlation between α and x (E(αi|Xi)=0),
specific variance covariance structure = Ω


what is the main thing that the fixed effects OLS allows for

correlation between αi and the regressors,
it is a positive function of x


how does fixed effects solve the endogeneity problem of alpha

by estimating the model including n individual intercepts, or a constant and n-1 dummies


what is still assumed for the fixed effects OLS even though it allows for correlation between α and x

FE OLS estimator is consistent, allowing for general, unspecified correlations between α and x, under the assumption of strict exogeneity of the regressors,


does the full model with n intercepts need to estimated for fixed effects OLS

the estimator just takes into account distances of variables from their means


describe fixed effects ols in a nice sentence

we simply regress the dependent variable in deviations from the individual specific means on the regressors in deviations of their respective individual specific means


what is one disadvantage of fixed effects ols compared to first differenced OLS

fixed effects OLS needs strict exogeneity so requires no correlation between xit and any of the idiosyncratic shocks uis,
so, this excludes feedback ie inputs xit do not respond to shocks in the past,


what is an assumption of fixed effects OLS in order for it to be efficient

If strong exogeneity holds, E(ui|Xi)=0 and the ui are further conditionally homoskedastic and not serially correlated,
fixed effects is the efficient estimator when allowing for general correlations between α and x


how does first differenced OLS solve the α issue

taking first differences over time for an individual eliminates αi


what are the consequences of first difference OLS to do with exogeneity

is a weaker assumption than strong exogeneity,
cannot have ut-1 affecting xt but can have ut-2 affecting xt,
first differenced so loss of n-1 degrees of freedom


do you have to do clustering or robust standard errors for first differenced

yes have to do clustering and robust standard errors,
robust allows for general serial correlation patterns and general forms of heteroskedasticity of the variances and covariances


how are common time effects taken care of in the panel data estimators

common time effects can be taken care of by time specific coefficients, inclusion of time dummies:


what do clustered standard errors do

account for heteroskedasticity across clusters of observations (states, schools or individuals), used when each unit observed across time, allows for correlations between observations