This paper studies the sensitivity of random effects estimators in the one-way error component regression model. Maddala and Mount (1973) [6] give simulation evidence that in random effects models the properties of the feasible GLS estimator β are not affected by the choice of the first-step ...
500.000 6725991.068462-1.530.150-1031.691176.1256f variance dueto u_i)20.10Prob F = 0.0000Number ofobs=28Number ofgroups=14xtreg c y, reRandom-effects GLS regressionGroup variable (i): nR-sq: within=0. 87 11、41between = 0.9297 overall = 0.9279Random effects corr(u_i, X)u_i Gaussian=...
I am surprised that this has even worked, but theresults may be meaningless. You might want to cluster householdsthat live in the same village, but there is no reason to duplicatethe adjustment for the panel variable. Ultimately, the suspicion has to be that the model which you areusing te...
SubjectRepost: instrumental variables regression with random effects GLS using cross-section data and endogenous binary independent variable DateThu, 4 Oct 2007 14:38:57 +0100 (BST) Dear Statalisters I have a cross-section data and my dependent variable is continuous and I have one independent ...
固定效应模型(FixedEffect或LSDV)Y it 由截距项体现个体差异模型(1)截距项i模型(2)iti,t非随机的 i X it it 随机效应模型（RandomEffect）itiititii YX截距项,随机的模型可以改写为：Y...
固定效应模型(FixedEffect或LSDV)Y it 由截距项体现个体差异模型(1)截距项i模型(2)iti,t非随机的 i X it it 随机效应模型（RandomEffect）itiititii YX截距项,随机的模型可以改写为：Y...
specific random effect (intercept) u(i), which by assumption is not correlated with the regressors x(i,t). In model 2) there is also an unobserved time specific random effect (intercept) w(t), which by assumption is not correlated with the regressors x(i,t). A GLS estimator ...
A GLS estimator (or ML assuming normality) is adopted to deal with the particular forms of correlation induced by the random effects (intercepts) in the two models. -xtreg,re- cannot estimate what is known (in econometrics) as the two-way random-effect model: y(i,t)=Beta*x(i,t)+ u...
We can obtain Amemiya–MaCurdy estimates of the coefficients in (2) and the conventional VCE by fitting an instrumental-variables regression of the GLS-transformed yi∗t on X∗it and Z∗it, using Xit, X˘ 1it, and Z1i as instruments, where X˘ 1it = X1i1, X1i2, . . . ...