Wednesday, May 1, 2013

Finite Sample Properties of GMM

In a comment on a post earlier today,  Stephen Gordon quite rightly questioned the use of GMM estimation with relatively small sample sizes. The GMM estimator is weakly consistent, the "t-test" statistics associated with the estimated parameters are asymptotically standard normal, and the J-test statistic is asymptotically chi-square distributed under the null. But what can be said in finite samples?

Of course, this question applies to almost all of the estimators that we use in practice - IV, MLE, GMM, etc. Indeed, lots of work has been done to explore the finite-sample properties of such estimators. For instance, consider my own work on bias corrections for MLEs (see here, here, and here). So, I'm more than sympathetic to the general point that Stephen made.

The example that I had provided, which was just a teaching example, used 175 quarterly observations. Is this enough for the asymptotics to "kick in" for GMM estimation of an Euler equation of the type I was considering?

My first reaction was: "let's just bootstrap this thing and find out." My second reaction was: "there has to be plenty of evidence out there already, so let's not re-invent the wheel." Indeed, this is the case. Several studies have examined the performance of GMM in precisely the context that I was using it in my own example.

Three relevant studies are those of Tauchen (1986), Kocherlakota (1990), and Hansen et al. (1996). There are probably others as well.

1. Tauchen considers sample sizes of 50 and 75 - much smaller than I was using. Among his conclusions (p.397):

  • "There is a variance/bias trade-off regarding the number of lags used to form instruments: with short lags, the estimates of utility function parameters are nearly asymptotically optimal, but with longer lags the estimates concentrate around biased values and confidence intervals become misleading." 
  • "The test of the overidentifying restrictions performs well in small samples; if anything, the test is biased toward acceptance of the null hypothesis."

2. Kocherlakota considers a sample of T = 90 observations. Among his conclusions:

  • The J-test exhibits minimal size distortion in four of the seven experimental designs considered, and is is biased toward over-rejection of the null in the other three cases. These outcomes depend very much on the choice of instruments.
  • In the three cases of over-rejection on the part of the J-test, the estimates of the parameters are downward median-biased. However, they are essentially median-unbiased in the other four cases considered.

3. Hansen et al. consider a sample size of 100 for the part of their study most relevant here. Among their conclusions:

  • The finite-sample properties of the GMM estimator depend very much on the way in which the moment conditions are weighted.
  • "Continuous updating in conjunction with criterion-function-based inference often performed better than other methods for annual data; however, the large-sample approximations are still not very reliable." (p.278)
  • "The continuous-updating estimator typically had less median bias than the other estimators, but the Monte Carlo sample distributions for this estimator sometimes had much fatter tails." (p.278) [This has implications for the coverage probability of confidence intervals; DG.]
  • "The test for overidentifying restrictions are, by construction, more conservative when the weighting matrix is continuously updated, and in many cases this led to a more reliable test statistic."

So, where does that leave us? I'm glad that I used the continuous-updating version of the GMM estimator in my illustration. With T = 175, I'm in a somewhat better position than those considered in the simulation studies I've just cited. However, the results should be treated very cautiously.

On the other hand, what are you going to do in practice? I think that Tauchen (1986, p.415) had the right idea: bootstrap the GMM estimates to compensate for small-sample bias, and to obtain appropriate confidence intervals and critical values for the J-test. 


Hansen, L. O., J. Heaton, & A. Yaron, 1996. Finite-sample properties of some GMM models. Journal of Business and Economic Statistics, 14, 262-280.

Kocherlakota, N. R., 1990. On tests of representative consumer asset pricing models. Journal of Monetary Economics, 26, 285-304.

Tauchen, G., 1986. Statistical properties of generalized method-of-moments estimators of structural parameters obtained from financial market data. Journal of Business and Economic Statistics, 4, 397-416 (plus discussion and response).

© 2013, David E. Giles

1 comment:

  1. Precisely, Bond has a paper discussing the small sample properties of his estimator.


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