<p>It is a fancy, confusing and indirect way to say that GMM, being a Method-of-Moments estimator, <em>does not require</em> distributional assumptions in order to estimate regression coefficients, just like Ordinary Least Squares, and in contrast to maximum likelihood that needs to make distributional assumptions. </p>

<p>The wording is confusing, because "robust to distributional assumptions" means that we get consistent estimates even if we have made the wrong distributional assumptions.</p>

CC BY-SA 4.0
2019-02-01T19:16:25.523Z
2019-02-04T13:00:13.153Z
2019-02-04T13:00:13.153Z
Alecos Papadopoulos
<p>[<img src="https://i.sstatic.net/FOThJ.png" alt="enter image description here"></p>

<p>What does it mean that GMM is robust to distributional assumptions?</p>

<econometrics><self-study>
features of Generalised Method of Moments estimation
Niko
# features of Generalised Method of Moments estimation

(Asked by: username_0 on 2019-02-01)

[![enter image description here](https://i.sstatic.net/FOThJ.png)

What does it mean that GMM is robust to distributional assumptions?

**Tags:** econometrics, self-study

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## 1 answers

### Answer by username_1 on 2019-02-01. Score: 3

It is a fancy, confusing and indirect way to say that GMM, being a Method-of-Moments estimator, *does not require* distributional assumptions in order to estimate regression coefficients, just like Ordinary Least Squares, and in contrast to maximum likelihood that needs to make distributional assumptions.

The wording is confusing, because "robust to distributional assumptions" means that we get consistent estimates even if we have made the wrong distributional assumptions.


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