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  "body": "\n## Summary\n\nThe method of estimating equations defines an estimator as a solution of $\\sum_i g(X_i, \\theta) = 0$.\nHere the function $g$ has mean zero at the true parameter.\nThe likelihood equations, the moment equations of the method of moments and the normal equations of least squares are all estimating equations.\nThe approach needs only a model for some moments, not a full probability model.\nIts asymptotic theory is the same as that of M-estimators, with a sandwich formula for the variance.\n\n## Explanations\n\n### L1 human\n\nWrite down an equation that the true parameter satisfies on average, such as \"the mean of $X - \\theta$ is zero\".\nReplace the average over the population by the average over the sample and solve for $\\theta$.\nIn the example this gives $\\theta = \\bar{X}$, the sample mean.\nBecause the sample average is close to the population average, the solution is close to the true parameter.\n",
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