Note: This FAQ is for users of Stata 11 It is not relevant for more recent versions.

Title | The divisor choice in xtgee | |

Author | David M. Drukker, StataCorp | |

Date | March 2000 |

**Technical Note:** This is a technical FAQ. Technical FAQs
address specific issues or computational aspects of estimators or other
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All of the materials in the Stata manual, including the Methods and Formulas
and sometimes the references, are assumed to be understood.

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- Divisors in
**xtgee** - Why do my
**xtgee**results differ from the results produced by SAS Genmod? - Code and instructions

The default divisor for computing correlations and standard errors in
**xtgee** is N, the
number of observations in the dataset. With this divisor, the estimates are
invariant to the scale of the dataset. Scale invariance is an important
property. No weights are equivalent to frequency weights of one. If you
multiply these weights by a scale factor, you would not want your estimates
to change.

In some other packages and in some previous versions of **xtgee**, the
divisor is N − p, where p is the number of covariates in the model.
This divisor is used to obtain unbiased estimates. This divisor, however,
causes the estimates not to be scale invariant. As N goes to infinity, the
difference between the two divisors goes to zero.

In **xtgee**, if you specify the option **nmp**, the divisor N −
p will be used instead of the default divisor N.

The scaling issue also affects the normalization factor for the robust VCE
when **family(gaussian)** is specified. Historically, **xtgee** used
{(npanels)/(npanels − 1)}{(N − 1)/(N − p)}, where npanels
is the number of panels in the dataset and N and p are defined as above.
This normalization factor would prevent the VCE from being invariant to the
scale of the weights. For this reason, the default normalization factor is
now (npanels)/(npanels − 1) instead of {(npanels)/(npanels − 1)}
{(N − 1)/(N − p)}. One can use the previous normalization
factor by specifying the option **rgf**.

The divisor used in computing the unstructured and nonstationary correlation
matrices for each element in the correlation matrix has changed to the
number of panels that have valid observations for the t_{i} and
t_{j} defined by that element.

With balanced data, **xtgee** and SAS version 6.12 Genmod will produce
the same results if the correct options are specified. For models with
**family(Gaussian)** and **link(identity)**, the default options for
each correlation structure produce matching results. For non-Gaussian
models, Stata sets the scale coefficient to 1 by default. In contrast, SAS
Genmod defaults to setting the scale parameter to the Pearson chi-squared.
One can match the SAS Genmod results with Stata’s **xtgee** by
specifying the **scale(x2)** option.

For unbalanced data, Stata’s **xtgee** and SAS version 6.12 Genmod
do not produce exactly the same results. Theoretically, GEE estimation with
**family(gaussian)**, **link(identity)**, and
**correlation(independent)** should produce the same results as simple
linear regression. Stata’s **xtgee** does reduce to simple OLS
under these conditions; however, SAS Genmod does not. Instead of

SAS Genmod is using

This difference indicates that SAS Genmod forces the panels to have the same weight instead of an implicit weighting determined by length as is done in the first formula.

For more complex correlation structures, it is not possible to back out the specific differences between the packages. Analogous differences in computing the scale parameter and the parameters of the correlation matrix are probably responsible for the differences in the results obtained by the two packages.

To investigate the behavior of Stata’s **xtgee**, I conducted a
Monte Carlo study with 10,000 replications. The data were generated from a
process

y_{it}=-1+x1_{it}+2*x2_{it}+3*x3_{it}+4*x4_{it}+e_{it}

where e_{it} is gaussian and autoregressive with a correlation
coefficient of .3.

On each of these datasets, I ran

. xtgee y x1 x2 x3 x4, family(gaussian) link(identity) i(id) corr(ar 1)

and saved the relevant information. (The code running these simulations can be obtained by clicking here.)

The following table gives the mean of the estimates that I obtained for the coefficients and the autoregression parameter over the 10,000 runs. The means of the estimates are all within .012 of the true parameter.

Variable | Obs Mean Std. Dev. Min Max ---------+----------------------------------------------------- x1 | 10000 1.011026 .9790695 -2.778255 4.613505 x2 | 10000 1.993917 .4733509 .3084104 3.762877 x3 | 10000 2.999791 .2484509 2.03008 3.949788 x4 | 10000 3.999901 .2136448 3.205784 4.835913 cons | 10000 -1.005913 .5871637 -3.055032 1.362445 rho | 10000 .2996235 .0167369 .2390387 .3540029

I then checked the *p*-values that resulted from a hypothesis test
against the null that coefficient was its true value. Since there were
10,000 runs, one expects there to be about 500 *p*-values less than or
equal to .05. The following table indicates that this is what I found.

Variable | # of P-Values <=.05 ----------|-------------------- x1 | 492 x2 | 489 x3 | 519 x4 | 515 _cons | 475

The results indicate that Stata’s **xtgee** is performing well on
unbalanced data.

To run the simulations first, obtain **ar_sim.ado** by
clicking here.
Make sure to save the program as **ar_sim.ado**. Then within Stata, use
the **simulate**
command as follows

. set mem 10m . simulate ar_sim, reps(10000)

For more information, see [R] **simulate**. Although exact execution
times will differ according to processor and disk drive speeds, running all
10,000 replications takes a long time.