The MM, ME, ML, EL, EF and GMM approaches to estimation: a synthesis

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Estimating Equations Inference With Missing Data (2008, background): “This is expected to result in improved finite-sample properties of estimators (Bera and Bilias 2002).”
Testing Distributional Assumptions: A GMM Approach (2007, methodology): “, Bollerslev, Engle and Nelson (1994). However, some empirical studies found that the implied kurtosis of a GARCH(1,1) is lower than empirical ones. These studies lead Bollerslev (1987) to assume that the standardized process ut may follow a Student distribution given in (5.3) where μ = 0. Under thi”
Generalized Method of Moments, Efficient Bootstrapping, and Improved Inference (2002, background): “…basic idea of choosing estimators to minimize a quadratic form in deviations between sample moments and population moments can be traced as far back as Pearson’s (1900) chi-squared statistic and the suggestion by Smith (1916) and others of minimum chi-square (see Bera and Bilias 2002 for a survey).”
Generalized M‐fluctuation tests for parameter instability (2007, background): “The visulizations of all these test statistics convey information about the timing of the change(s) (if any) and are powerful exploratory tools in the spirit of Brown et al. (1975) (see Section 5 for several examples).”