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st: GMM bug when clustering on string var
From
Julian Reif <[email protected]>
To
[email protected]
Subject
st: GMM bug when clustering on string var
Date
Thu, 7 Jul 2011 11:41:40 -0500
The -gmm- command appears to be reporting incorrect standard errors when clustering on a string variable. I am running Stata 11.2 on Windows XP. The following code gives an example:
---------------------
---------------------
clear all
set more off
set seed 29
set obs 100
gen i = _n
gen gamma_i = invnorm(uniform()) + 3
expand 10
bysort i: gen t = _n
gen x1 = invnorm(uniform())*5
gen e = invnorm(uniform())
gen y = 3*x1 + gamma_i + e
gen string_i = string(i)
* Same results for standard errors
reg y x1, vce(cluster i)
reg y x1, vce(cluster string_i)
* These two commands report different results for standard errors
gmm (y - {b1}*x1 - {b0}), instruments(x1) onestep vce(cluster i)
gmm (y - {b1}*x1 - {b0}), instruments(x1) onestep vce(cluster string_i)
---------------------
---------------------
Here is the output from the -reg- and -gmm- commands:
. * Same results for standard errors
.
. reg y x1, vce(cluster i)
Linear regression Number of obs = 1000
F( 1, 99) = .
Prob > F = 0.0000
R-squared = 0.9913
Root MSE = 1.3998
(Std. Err. adjusted for 100 clusters in i)
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 2.998964 .007909 379.18 0.000 2.983271 3.014658
_cons | 3.042796 .1064039 28.60 0.000 2.831667 3.253924
------------------------------------------------------------------------------
.
. reg y x1, vce(cluster string_i)
Linear regression Number of obs = 1000
F( 1, 99) = .
Prob > F = 0.0000
R-squared = 0.9913
Root MSE = 1.3998
(Std. Err. adjusted for 100 clusters in string_i)
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 2.998964 .007909 379.18 0.000 2.983271 3.014658
_cons | 3.042796 .1064039 28.60 0.000 2.831667 3.253924
------------------------------------------------------------------------------
.
.
.
. * These two commands report different results for standard errors
.
. gmm (y - {b1}*x1 - {b0}), instruments(x1) onestep vce(cluster i)
Step 1
Iteration 0: GMM criterion Q(b) = 232.29144
Iteration 1: GMM criterion Q(b) = 9.907e-25
Iteration 2: GMM criterion Q(b) = 2.820e-32
GMM estimation
Number of parameters = 2
Number of moments = 2
Initial weight matrix: Unadjusted Number of obs = 1000
(Std. Err. adjusted for 100 clusters in i)
------------------------------------------------------------------------------
| Robust
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
/b1 | 2.998964 .0078654 381.29 0.000 2.983549 3.01438
/b0 | 3.042796 .1058175 28.76 0.000 2.835397 3.250194
------------------------------------------------------------------------------
Instruments for equation 1: x1 _cons
.
. gmm (y - {b1}*x1 - {b0}), instruments(x1) onestep vce(cluster string_i)
Step 1
Iteration 0: GMM criterion Q(b) = 232.29144
Iteration 1: GMM criterion Q(b) = 9.907e-25
Iteration 2: GMM criterion Q(b) = 2.820e-32
GMM estimation
Number of parameters = 2
Number of moments = 2
Initial weight matrix: Unadjusted Number of obs = 1000
(Std. Err. adjusted for 1 clusters in string_i)
------------------------------------------------------------------------------
| Robust
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
/b1 | 2.998964 2.85e-17 1.1e+17 0.000 2.998964 2.998964
/b0 | 3.042796 6.46e-17 4.7e+16 0.000 3.042796 3.042796
------------------------------------------------------------------------------
Instruments for equation 1: x1 _cons
*
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