Ignore my last post. In this case there is no such transformation.
===========================================
Alfonso Miranda
PhD Student
Economics Department
University of Warwick
Coventry CV4 7AL
E-mail: [email protected]
===========================================
>>> [email protected] 02/24/04 16:03 PM >>>
I guess sigma_u = log(sigma). In many cases Stata estimates a function of an auxiliary parameter instead of the parameter itself. This thick helps to keep estimates whithin a set of admissible values and ease ML maximisation. In any case, you could always recover the value of the parameter in the original scale applying the inverse function. Standard errors may be obtained using the delta method.
===========================================
Alfonso Miranda
PhD Student
Economics Department
University of Warwick
Coventry CV4 7AL
E-mail: [email protected]
===========================================
>>> [email protected] 02/24/04 15:39 PM >>>
I'm very confused about why the sigma_u listed in the output from a random
effects model reports a value of zero, despite the fact that I have unit
variance in my dataset (I promise). I'm attaching the output below. Could
someone please tell me what this means? Thanks!
xtreg dppct educpct repubpct fundpct whitepct, re;
Random-effects GLS regression Number of obs = 220
Group variable (i) : cohort Number of groups = 10
R-sq: within = 0.0577 Obs per group: min = 22
between = 0.6344 avg = 22.0
overall = 0.0622 max = 22
Random effects u_i ~ Gaussian Wald chi2(4) = 14.27
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0065
------------------------------------------------------------------------------
dppct | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educpct | .0596706 .0526787 1.13 0.257 -.0435777 .1629189
repubpct | .2223392 .1046384 2.12 0.034 .0172516 .4274267
fundpct | .2698267 .1397987 1.93 0.054 -.0041737 .543827
whitepct | .2623254 .1236531 2.12 0.034 .0199697 .5046811
_cons | .2656249 .1358642 1.96 0.051 -.000664 .5319138
-------------+----------------------------------------------------------------
sigma_u | 0
sigma_e | .09882399
rho | 0 (fraction of variance due to u_i)
------------------------------------------------------------------------------
Amber E. Boydstun
Graduate Student
Department of Political Science
Penn State University
University Park, PA 16802-6200
[email protected]
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