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First thought: This is a multidisciplinary list, so spelling out ROE as
"Return on Equity" may make sense.
You may want to look at Roodman´s comprehensive article in the SJ
http://www.stata-journal.com/article.html?article=st0159
The warning you complained about can be found three times in there...
HTH
Martin
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Christian
Stammschulte
Sent: Sonntag, 7. Februar 2010 10:28
To: [email protected]
Subject: st: Persistency measurement on unbalanced panel (dynamic panel
regression)
Dear Stata Users,
I am new to STATA and I am currently trying to investigate profit
persistency patterns.
Given an unbalanced panel, short time periods and 3,000 companies for
investigation with demeaned RoEs I was trying to estimate persistency by
applying xtabond2 with three lags, twostep, robust and orthogonal approach.
However, two questions on this:
(1) I am receiving the error message "Two-step estimated covariance matrix
of moments is singular." Could you briefly explain to me why this is the
case?
(2) My Sargan and Hansen test are showing p-values of 0.0000. Since my
dataset only comprises RoEs/ lags in the model I assume this is the reason.
However, since I am only interested in persistency measurement I start
questioning whether the given procedure it is the right way to do it or
whether I am missing anything?
You will find the STATA output below.
Any help/ thoughts would be much appreciated.
Best regards and many thanks
Christian
*** STATA Output ***
. xtabond2 demeaned_win99_roae_ L.demeaned_win99_roae_
L2.demeaned_win99_roae_ L3.demeaned_win99_roae_,
gmmstyle(L.demeaned_win99_roae_) twostep robust orthogonal
Favoring space over speed. To switch, type or click on mata: mata set
matafavor speed, perm.
Warning: Two-step estimated covariance matrix of moments is singular.
Using a generalized inverse to calculate optimal weighting matrix for
two-step estimation.
Difference-in-Sargan/Hansen statistics may be negative.
Dynamic panel-data estimation, two-step system GMM
----------------------------------------------------------------------------
--
Group variable: id Number of obs =
25084
Time variable : year Number of groups =
3375
Number of instruments = 87 Obs per group: min =
1
Wald chi2(3) = 1103.60 avg =
7.43
Prob > chi2 = 0.000 max =
11
----------------------------------------------------------------------------
--
| Corrected
demeaned_w~_ | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
demeaned_w~_ |
L1. | .539917 .0177421 30.43 0.000 .505143
.5746909
L2. | .1872978 .0155508 12.04 0.000 .1568188
.2177768
L3. | .0594477 .0123591 4.81 0.000 .0352244
.0836711
|
_cons | -.0004252 .0003175 -1.34 0.180 -.0010475
.0001971
----------------------------------------------------------------------------
--
Instruments for orthogonal deviations equation
GMM-type (missing=0, separate instruments for each period unless
collapsed)
L(1/.).L.demeaned_win99_roae_
Instruments for levels equation
Standard
_cons
GMM-type (missing=0, separate instruments for each period unless
collapsed)
D.L.demeaned_win99_roae_
----------------------------------------------------------------------------
--
Arellano-Bond test for AR(1) in first differences: z = -18.61 Pr > z =
0.000
Arellano-Bond test for AR(2) in first differences: z = -0.59 Pr > z =
0.557
----------------------------------------------------------------------------
--
Sargan test of overid. restrictions: chi2(83) = 624.28 Prob > chi2 =
0.000
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(83) = 248.87 Prob > chi2 =
0.000
(Robust, but can be weakened by many instruments.)
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group: chi2(72) = 214.50 Prob > chi2 =
0.000
Difference (null H = exogenous): chi2(11) = 34.37 Prob > chi2 =
0.000
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