Further results on projection-based inference in IV regressions with weak, collinear or missing instruments ∗

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SPECIFICATION TESTING IN MODELS WITH MANY INSTRUMENTS (2010, unknown): “The Anderson–Rubin statistic possesses some appealing robustness properties (Dufour and Taamouti, 2007) and is χ2( ) distributed under fixed asymptotics.”
A new method of projection-based inference in GMM with weakly identified nuisance parameters (2011, methodology): “…unlike the usual technique of projection found in the weak identification literature [see, among others, Dufour (1997), Dufour and Jasiak (2001), Dufour and Taamouti (2005, 2007)], we project from a subset of the parameter space of the nuisance parameters θ2, restricted by the null hypothesis…”
Wild Bootstrap Tests for IV Regression (2010, background): “These include the famous Anderson–Rubin, or AR, statistic proposed by Anderson and Rubin (1949) and extended by Dufour and Taamouti (2005, 2007), the Lagrange Multiplier, or K, statistic proposed by Kleibergen (2002), and the conditional likelihood ratio, or CLR, test proposed by Moreira (2003).”