The Large Sample Behaviour of the Generalized Method of Moments Estimator in Misspecified Models

By A. Hall, A. Inoue. 2003

186 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e8b9a9da5c96b2ce4f2656f9de876abc711fffb3

How researchers see this paper

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In Statistics and Probability

1. Xiaohong Chen
2. Liangjun Su
3. B. Hansen
4. T. Armstrong
5. Chunrong Ai
6. Michael J. Kolesar
7. Seojeong Lee
8. Stefan Wager

Citations in the corpus

• Estimation of possibly misspecified semiparametric conditional moment restriction models with different conditioning variables (2007, background): “This is the case even for the parametric (e.g., α = θ), misspecified and overidentified unconditional moment models; for such models Hall and Inoue (2003) show that the limiting distribution of bθn depends on the asymptotic distribution of the estimated weighting matrix.”
• Asymptotic theory for clustered samples (2019, unknown): “For example, the asymptotic distribution of the generalized method of moments (GMM) estimators depends on the limiting distribution of the weight matrix when the moment condition is misspecified (Hall and Inoue, 2003; Lee, 2014; Hansen and Lee, 2018).”
• Identification and inference in linear stochastic discount factor models with excess returns (2016, background): “If these moment conditions are not satisfied, theorems for misspecified models from Hall and Inoue (2003) and Hall (2005) apply.”
• Sensitivity analysis using approximate moment condition models (2018, methodology): “Hall and Inoue (2003) show that under regularity conditions, if C̃ is fixed with n, √ n(ĥ− h(θ∗ 0)) d → N (0,Ωh),”
• The validity of instruments revisited (2012, unknown): “Similar assumptions about the violation of exogeneity have been used by Newey (1985) and Hall and Inoue (2003).”
• Simulated Method of Moments Estimation for Copula-Based Multivariate Models (2013, unknown): “Hall and Inoue (2003) showed that the limit distribution of GMM (with smooth, differentiable moment functions) depends on the limit distribution of the weight matrix, not merely the probability limit of the weight matrix.”
• Inference for Iterated GMM Under Misspecification (2021, background): “The CLT can be applied to (22) which has variance”