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Re: st: testing fixed effects versus random effects for clustered data using overiden
From
Gordon Hughes <[email protected]>
To
[email protected]
Subject
Re: st: testing fixed effects versus random effects for clustered data using overiden
Date
Tue, 01 Mar 2011 11:42:50 +0000
A somewhat belated response, but you need to think more carefully
about what you are trying to do. Some points to bear in mind:
A. Why are you trying to test FE against RE for your data? I know
it is standard in this literature, but does it make sense in this case?
B. Look at your models. The only worthwhile explanatory variables
are soil_type_1 & soil_type_2. The classic justification for the FE
specification is correlation between the individual effect and some
of the explanatory variables, perhaps due to omitted variable
bias. Do you have reason to believe that, for example, better or
worse farmers tend to have plots with better or worse soil
quality? It is possible, but this is the type of question to think about.
C. What are your options if you conclude that FE is the better
specification? You have 86 households with an average of 2.6 plots
per household, so you will lose 40% of your data. You need to think
about alternative specifications that are less vulnerable to the
problems that lead the RE estimates to be inconsistent. You might
look at Chapter 21 in the Cameron & Trivedi text on Microeconometrics
plus the references cited there.
D. Why exactly are you combining robust panel estimation with
cluster-adjusted standard errors using the panel variable as the
cluster variable? I am surprised that this has even worked, but the
results may be meaningless. You might want to cluster households
that live in the same village, but there is no reason to duplicate
the adjustment for the panel variable.
Ultimately, the suspicion has to be that the model which you are
using tells you little more than that soil types influence crop
yields. This may or may not be correct, but the solution lies in a
better specification of your model, not in trying an ever larger
range of econometric techniques to a limited and noisy set of data.
Gordon Hughes
[email protected]
------------------------------
Date: Mon, 28 Feb 2011 14:46:15 +0000
From: Ridhima Gupta <[email protected]>
Subject: st: testing fixed effects versus random effects for
clustered data using overiden
Hello,
I don't have a panel data in the strict sense of the term i.e. I have
data on farmers who have several plots/fields. I first perform a
standard hausman test and I do not reject the
null hypothesis of random effects. The result is as follows:
hausman fixed ., sigmamore
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fixed . Difference S.E.
-
-------------+----------------------------------------------------------------
Happy_Seeder | .1271123 .5428827 -.4157704 .429518
Rotavator | -.3621306 -.3157851 -.0463455 .840699
Seed_Drill | -.083707 .860967 -.944674 .5539517
quantity_s~c | .0056369 -.0031733 .0088101 .0188915
plotsize_hec | .0252054 .0310519 -.0058465 .0525993
ferti_hec | .0027516 .003003 -.0002514 .0045816
exp_weedi_~s | .1077859 .0282141 .0795719 .0585852
soil_type_1 | -1.034988 -1.900228 .8652395 .6801256
soil_type_2 | -.4202215 -2.165068 1.744846 .8051986
-
------------------------------------------------------------------------------
b = consistent under Ho and Ha; obtained
from xtreg
B = inconsistent under Ha, efficient under Ho; obtained
from xtreg
Test: Ho: difference in coefficients not systematic
chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 12.70
Prob>chi2 = 0.1766
But when I perform the robust version of this test, I reject the null
hypothesis of random effects.
Random-effects GLS regression Number of obs = 227
Group variable: hh_id Number of groups = 86
R-sq: within = 0.0106 Obs per group: min = 1
between = 0.0922 avg
= 2.6
overall = 0.0674 max
= 6
Random effects u_i ~ Gaussian Wald chi2(9) = 15.76
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0720
(Std. Err. adjusted for 86
clusters in hh_id)
-
------------------------------------------------------------------------------
| Robust
yield_per_~c | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-
-------------+----------------------------------------------------------------
Happy_Seeder | .5428827 1.024596 0.53 0.596 -1.465289 2.551054
Rotavator
| -.3157851 1.425605 -0.22 0.825 -3.10992 2.47835
Seed_Drill
| .860967 1.036252 0.83 0.406 -1.170049 2.891983
quantity_s~c | -.0031733 .041733 -0.08 0.939 -.0849685 .078622
plotsize_hec | .0310519 .0467653 0.66 0.507 -.0606065 .1227103
ferti_hec
| .003003 .0053299 0.56 0.573 -.0074435 .0134495
exp_weedi_~s | .0282141 .0792311 0.36 0.722 -.1270759 .1835041
soil_type_1
| -1.900228 .7639786 -2.49 0.013 -3.397599 -.4028574
soil_type_2
| -2.165068 .8668351 -2.50 0.013 -3.864033 -.4661022
_cons
| 41.85728 4.370738 9.58 0.000 33.29079 50.42376
-
-------------+----------------------------------------------------------------
sigma_u | 4.4041886
sigma_e | 4.0351113
rho | .54364968 (fraction of variance due to u_i)
-
------------------------------------------------------------------------------
. xtoverid
Test of overidentifying restrictions: fixed vs random effects
Cross-section time-series model: xtreg re robust cluster(hh_id)
Sargan-Hansen statistic 26.327 Chi-sq(9) P-value = 0.0018
Is there any inconsistency here? Are there any tests in the literature
that allow me to test the assumption of homoskedasticity and no
auto-correlation in the random effects model?
Thanks a lot,
Ridhima
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