## Understanding the generalized method of moments (GMM): A simple example

\(\newcommand{\Eb}{{\bf E}}\)This post was written jointly with Enrique Pinzon, Senior Econometrician, StataCorp.

The generalized method of moments (**GMM**) is a method for constructing estimators, analogous to maximum likelihood (**ML**). **GMM** uses assumptions about specific moments of the random variables instead of assumptions about the entire distribution, which makes **GMM** more robust than **ML**, at the cost of some efficiency. The assumptions are called moment conditions.

**GMM** generalizes the method of moments (**MM**) by allowing the number of moment conditions to be greater than the number of parameters. Using these extra moment conditions makes **GMM** more efficient than **MM**. When there are more moment conditions than parameters, the estimator is said to be overidentified. **GMM** can efficiently combine the moment conditions when the estimator is overidentified.

We illustrate these points by estimating the mean of a \(\chi^2(1)\) by **MM**, **ML**, a simple **GMM** estimator, and an efficient **GMM** estimator. This example builds on Efficiency comparisons by Monte Carlo simulation and is similar in spirit to the example in Wooldridge (2001).

**GMM weights and efficiency**

**GMM** builds on the ideas of expected values and sample averages. Moment conditions are expected values that specify the model parameters in terms of the true moments. The sample moment conditions are the sample equivalents to the moment conditions. **GMM** finds the parameter values that are closest to satisfying the sample moment conditions.

The mean of a \(\chi^2\) random variable with \(d\) degree of freedom is \(d\), and its variance is \(2d\). Two moment conditions for the mean are thus

\[\begin{eqnarray*}

\Eb\left[Y – d \right]&=& 0 \\

\Eb\left[(Y – d )^2 – 2d \right]&=& 0

\end{eqnarray*}\]

The sample moment equivalents are

\[\begin{eqnarray}

1/N\sum_{i=1}^N (y_i – \widehat{d} )&=& 0 \tag{1} \\

1/N\sum_{i=1}^N\left[(y_i – \widehat{d} )^2 – 2\widehat{d}\right] &=& 0 \tag{2}

\end{eqnarray}\]

We could use either sample moment condition (1) or sample moment condition (2) to estimate \(d\). In fact, below we use each one and show that (1) provides a much more efficient estimator.

When we use both (1) and (2), there are two sample moment conditions and only one parameter, so we cannot solve this system of equations. **GMM** finds the parameters that get as close as possible to solving weighted sample moment conditions.

Uniform weights and optimal weights are two ways of weighting the sample moment conditions. The uniform weights use an identity matrix to weight the moment conditions. The optimal weights use the inverse of the covariance matrix of the moment conditions.

We begin by drawing a sample of a size 500 and use **gmm** to estimate the parameters using sample moment condition (1), which we illustrate is the sample as the sample average.

. drop _all . set obs 500 number of observations (_N) was 0, now 500 . set seed 12345 . generate double y = rchi2(1) . gmm (y - {d}) , instruments( ) onestep Step 1 Iteration 0: GMM criterion Q(b) = .82949186 Iteration 1: GMM criterion Q(b) = 1.262e-32 Iteration 2: GMM criterion Q(b) = 9.545e-35 note: model is exactly identified GMM estimation Number of parameters = 1 Number of moments = 1 Initial weight matrix: Unadjusted Number of obs = 500 ------------------------------------------------------------------------------ | Robust | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- /d | .9107644 .0548098 16.62 0.000 .8033392 1.01819 ------------------------------------------------------------------------------ Instruments for equation 1: _cons . mean y Mean estimation Number of obs = 500 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ y | .9107644 .0548647 .8029702 1.018559 --------------------------------------------------------------

The sample moment condition is the product of an observation-level error function that is specified inside the parentheses and an instrument, which is a vector of ones in this case. The parameter \(d\) is enclosed in curly braces **{}**. We specify the **onestep** option because the number of parameters is the same as the number of moment conditions, which is to say that the estimator is exactly identified. When it is, each sample moment condition can be solved exactly, and there are no efficiency gains in optimally weighting the moment conditions.

We now illustrate that we could use the sample moment condition obtained from the variance to estimate \(d\).

. gmm ((y-{d})^2 - 2*{d}) , instruments( ) onestep Step 1 Iteration 0: GMM criterion Q(b) = 5.4361161 Iteration 1: GMM criterion Q(b) = .02909692 Iteration 2: GMM criterion Q(b) = .00004009 Iteration 3: GMM criterion Q(b) = 5.714e-11 Iteration 4: GMM criterion Q(b) = 1.172e-22 note: model is exactly identified GMM estimation Number of parameters = 1 Number of moments = 1 Initial weight matrix: Unadjusted Number of obs = 500 ------------------------------------------------------------------------------ | Robust | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- /d | .7620814 .1156756 6.59 0.000 .5353613 .9888015 ------------------------------------------------------------------------------ Instruments for equation 1: _cons

While we cannot say anything definitive from only one draw, we note that this estimate is further from the truth and that the standard error is much larger than those based on the sample average.

Now, we use **gmm** to estimate the parameters using uniform weights.

. matrix I = I(2) . gmm ( y - {d}) ( (y-{d})^2 - 2*{d}) , instruments( ) winitial(I) onestep Step 1 Iteration 0: GMM criterion Q(b) = 6.265608 Iteration 1: GMM criterion Q(b) = .05343812 Iteration 2: GMM criterion Q(b) = .01852592 Iteration 3: GMM criterion Q(b) = .0185221 Iteration 4: GMM criterion Q(b) = .0185221 GMM estimation Number of parameters = 1 Number of moments = 2 Initial weight matrix: user Number of obs = 500 ------------------------------------------------------------------------------ | Robust | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- /d | .7864099 .1050692 7.48 0.000 .5804781 .9923418 ------------------------------------------------------------------------------ Instruments for equation 1: _cons Instruments for equation 2: _cons

The first set of parentheses specifies the first sample moment condition, and the second set of parentheses specifies the second sample moment condition. The options **winitial(I)** and **onestep** specify uniform weights.

Finally, we use **gmm** to estimate the parameters using two-step optimal weights. The weights are calculated using first-step consistent estimates.

. gmm ( y - {d}) ( (y-{d})^2 - 2*{d}) , instruments( ) winitial(I) Step 1 Iteration 0: GMM criterion Q(b) = 6.265608 Iteration 1: GMM criterion Q(b) = .05343812 Iteration 2: GMM criterion Q(b) = .01852592 Iteration 3: GMM criterion Q(b) = .0185221 Iteration 4: GMM criterion Q(b) = .0185221 Step 2 Iteration 0: GMM criterion Q(b) = .02888076 Iteration 1: GMM criterion Q(b) = .00547223 Iteration 2: GMM criterion Q(b) = .00546176 Iteration 3: GMM criterion Q(b) = .00546175 GMM estimation Number of parameters = 1 Number of moments = 2 Initial weight matrix: user Number of obs = 500 GMM weight matrix: Robust ------------------------------------------------------------------------------ | Robust | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- /d | .9566219 .0493218 19.40 0.000 .8599529 1.053291 ------------------------------------------------------------------------------ Instruments for equation 1: _cons Instruments for equation 2: _cons

All four estimators are consistent. Below we run a Monte Carlo simulation to see their relative efficiencies. We are most interested in the efficiency gains afforded by optimal **GMM**. We include the sample average, the sample variance, and the ML estimator discussed in Efficiency comparisons by Monte Carlo simulation. Theory tells us that the optimally weighted **GMM** estimator should be more efficient than the sample average but less efficient than the ML estimator.

The code below for the Monte Carlo builds on Efficiency comparisons by Monte Carlo simulation, Maximum likelihood estimation by mlexp: A chi-squared example, and Monte Carlo simulations using Stata. Click gmmchi2sim.do to download this code.

. clear all . set seed 12345 . matrix I = I(2) . postfile sim d_a d_v d_ml d_gmm d_gmme using efcomp, replace . forvalues i = 1/2000 { 2. quietly drop _all 3. quietly set obs 500 4. quietly generate double y = rchi2(1) 5. . quietly mean y 6. local d_a = _b[y] 7. . quietly gmm ( (y-{d=`d_a'})^2 - 2*{d}) , instruments( ) /// > winitial(unadjusted) onestep conv_maxiter(200) 8. if e(converged)==1 { 9. local d_v = _b[d:_cons] 10. } 11. else { 12. local d_v = . 13. } 14. . quietly mlexp (ln(chi2den({d=`d_a'},y))) 15. if e(converged)==1 { 16. local d_ml = _b[d:_cons] 17. } 18. else { 19. local d_ml = . 20. } 21. . quietly gmm ( y - {d=`d_a'}) ( (y-{d})^2 - 2*{d}) , instruments( ) /// > winitial(I) onestep conv_maxiter(200) 22. if e(converged)==1 { 23. local d_gmm = _b[d:_cons] 24. } 25. else { 26. local d_gmm = . 27. } 28. . quietly gmm ( y - {d=`d_a'}) ( (y-{d})^2 - 2*{d}) , instruments( ) /// > winitial(unadjusted, independent) conv_maxiter(200) 29. if e(converged)==1 { 30. local d_gmme = _b[d:_cons] 31. } 32. else { 33. local d_gmme = . 34. } 35. . post sim (`d_a') (`d_v') (`d_ml') (`d_gmm') (`d_gmme') 36. . } . postclose sim . use efcomp, clear . summarize Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- d_a | 2,000 1.00017 .0625367 .7792076 1.22256 d_v | 1,996 1.003621 .1732559 .5623049 2.281469 d_ml | 2,000 1.002876 .0395273 .8701175 1.120148 d_gmm | 2,000 .9984172 .1415176 .5947328 1.589704 d_gmme | 2,000 1.006765 .0540633 .8224731 1.188156

The simulation results indicate that the ML estimator is the most efficient (**d_ml**, std. dev. 0.0395), followed by the efficient **GMM** estimator (**d_gmme**}, std. dev. 0.0541), followed by the sample average (**d_a**, std. dev. 0.0625), followed by the uniformly-weighted **GMM** estimator (**d_gmm**, std. dev. 0.1415), and finally followed by the sample-variance moment condition (**d_v**, std. dev. 0.1732).

The estimator based on the sample-variance moment condition does not converge for 4 of 2,000 draws; this is why there are only 1,996 observations on **d_v** when there are 2,000 observations for the other estimators. These convergence failures occurred even though we used the sample average as the starting value of the nonlinear solver.

For a better idea about the distributions of these estimators, we graph the densities of their estimates.

**Figure 1: Densities of the estimators**

The density plots illustrate the efficiency ranking that we found from the standard deviations of the estimates.

The uniformly weighted **GMM** estimator is less efficient than the sample average because it places the same weight on the sample average as on the much less efficient estimator based on the sample variance.

In each of the overidentified cases, the **GMM** estimator uses a weighted average of two sample moment conditions to estimate the mean. The first sample moment condition is the sample average. The second moment condition is the sample variance. As the Monte Carlo results showed, the sample variance provides a much less efficient estimator for the mean than the sample average.

The **GMM** estimator that places equal weights on the efficient and the inefficient estimator is much less efficient than a **GMM** estimator that places much less weight on the less efficient estimator.

We display the weight matrix from our optimal **GMM** estimator to see how the sample moments were weighted.

. quietly gmm ( y - {d}) ( (y-{d})^2 - 2*{d}) , instruments( ) winitial(I) . matlist e(W), border(rows) ------------------------------------- | 1 | 2 | _cons | _cons -------------+-----------+----------- 1 | | _cons | 1.621476 | -------------+-----------+----------- 2 | | _cons | -.2610053 | .0707775 -------------------------------------

The diagonal elements show that the sample-mean moment condition receives more weight than the less efficient sample-variance moment condition.

**Done and undone**

We used a simple example to illustrate how **GMM** exploits having more equations than parameters to obtain a more efficient estimator. We also illustrated that optimally weighting the different moments provides important efficiency gains over an estimator that uniformly weights the moment conditions.

Our cursory introduction to **GMM** is best supplemented with a more formal treatment like the one in Cameron and Trivedi (2005) or Wooldridge (2010).

**Graph code appendix**

use efcomp local N = _N kdensity d_a, n(`N') generate(x_a den_a) nograph kdensity d_v, n(`N') generate(x_v den_v) nograph kdensity d_ml, n(`N') generate(x_ml den_ml) nograph kdensity d_gmm, n(`N') generate(x_gmm den_gmm) nograph kdensity d_gmme, n(`N') generate(x_gmme den_gmme) nograph twoway (line den_a x_a, lpattern(solid)) /// (line den_v x_v, lpattern(dash)) /// (line den_ml x_ml, lpattern(dot)) /// (line den_gmm x_gmm, lpattern(dash_dot)) /// (line den_gmme x_gmme, lpattern(shordash))

**References**

Cameron, A. C., and P. K. Trivedi. 2005. *Microeconometrics: Methods and applications*. Cambridge: Cambridge University Press.

Wooldridge, J. M. 2001. Applications of generalized method of moments estimation. *Journal of Economic Perspectives* 15(4): 87-100.

Wooldridge, J. M. 2010. *Econometric Analysis of Cross Section and Panel Data*. 2nd ed. Cambridge, Massachusetts: MIT Press.