Stata’s **gmm** makes generalized method of moments
estimation as simple as nonlinear least-squares estimation and nonlinear seemingly
unrelated regression. Just specify your residual equations by using
substitutable expressions, list your instruments, select a weight
matrix, and obtain your results.

Here we fit a Poisson model of the number of doctor visits as a function of gender, income, and whether a person has a chronic disease or private health insurance. We have reason to believe that income is endogenous, so we use age and race as instruments.

. gmm (docvis - exp({xb:private chronic female income}+{b0})),>instruments(private chronic female age black hispanic) nologFinal GMM criterion Q(b) = .0021591 GMM estimation Number of parameters = 5 Number of moments = 7 Initial weight matrix: Unadjusted Number of obs = 4412 GMM weight matrix: Robust

Robust | ||

Coef. Std. Err. z P>|z| [95% Conf. Interval] | ||

private | .535335 .1599039 3.35 0.001 .2219291 .8487409 | |

chronic | 1.090126 .0617659 17.65 0.000 .9690668 1.211185 | |

female | .6636579 .0959884 6.91 0.000 .4755241 .8517918 | |

income | .0142855 .0027162 5.26 0.000 .0089618 .0196092 | |

/b0 | -.5983477 .138433 -4.32 0.000 -.8696713 -.327024 | |

By default, **gmm** uses the two-step estimator and a weight matrix
that assumes the errors are independent but not identically distributed. By using the **wmatrix()** and **vce()**
options, you can request weight and variance–covariance matrices
appropriate for errors that are independent and identically
distributed, are independent but not identically distributed, exhibit
group-level clustering, or that exhibit heteroskedasticity and
autocorrelation. In addition, you can obtain standard errors via
the bootstrap or the jackknife.

When fitting a model by GMM, you should check to see whether the
instruments you use really satisfy the orthogonality condition—i.e.,
whether they are uncorrelated with the errors. In GMM estimation,
Hansen’s *J* statistic is the most common test statistic. In our
example, whether our instruments are valid is certainly open for
debate—age likely influences the number of doctor visits—and we can
test their validity by using **estat overid** to obtain Hansen’s *J*
statistic:

. estat overidTest of overidentifying restriction: Hansen’s J chi2(2) = 9.52598 (p = 0.0085)

The test statistic has a *χ*^{2} distribution under the null hypothesis
that the instruments are valid. The significant statistic indicates
that one or more of our instruments are not valid (assuming that the
model is otherwise correctly specified).

**gmm**’s numerical derivative routines are very accurate, so most of
the time you do not need to spend time taking analytic derivatives.
However, if speed is of the essence or if you plan to fit the same model
repeatedly, analytic derivatives can be a boon. **gmm** provides a
simple way to specify derivatives. We could fit our previous model
with analytic derivatives by specifying

. gmm (docvis - exp({xb:private chronic female income}+{b0})),>instruments(private chronic female age black hispanic) nolog

The notation **{xb:private chronic female income}** is a shorthand to
create a linear combination of variables; we could have specified
**{xb_private}*private + {xb_chronic}*chronic + {xb_female}*female +
{xb_income}*income** and declared each parameter explicitly. By using
the shorter notation, we need only specify the derivative with respect
to the entire linear combination instead of each parameter individually.
Moreover, once we declare our linear combination, we can subsequently refer to
it as **xb:** without having to specify the variables associated with it.

In addition to standard instruments, **gmm** allows you to create
panel-style instruments used in dynamic and other panel models with
endogenous regressors. In these models, you typically use lagged
values of regressors as instruments; the number of lagged values
available increases as the time dimension increases. **gmm**’s
**xtinstruments()** option makes creating these instruments a snap.

For more complicated analyses, **gmm** allows you to write a program
to evaluate your residual equations instead of using substitutable
expressions. These programs are structured like those that
**ml**,
**nl**, and
**nlsur** use.
Here is our Poisson model fit using
the moment-evaluator program version of **gmm**:

program poisson_ex syntax varlist [if], at(name) myrhs(varlist) mylhs(varname) tempvar xb quietly { generate double `xb' = 0 `if' local i = 1 foreach var of varlist `myrhs' { replace `xb' = `xb' + `at'[1,`i']*`var' `if' local `++i' } replace `xb' = `xb' + `at'[1,`i'] `if' // constant term replace `varlist' = `mylhs' - exp(`xb') `if' } end gmm poisson_ex, nequations(1) nparameters(5) mylhs(docvis) /// myrhs(private chronic female income) /// instruments(private chronic female age black hispanic)

The programmable version of **gmm** is eminently flexible. You just
need to tell it how many moment equations you have and the number of
parameters in your model. You can optionally specify instruments and
select the type of weight matrix to use and standard errors to report.