ON THE COMPLETENESS CONDITION IN NONPARAMETRIC INSTRUMENTAL PROBLEMS

By X. d'Haultfoeuille. Econometric Theory, 2010

174 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2f1a1795ac79ea19ddfd08489778b91226cab7f8

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Citations in the corpus

A confounding bridge approach for double negative control inference on causal effects (2018, unknown): “As suggested by Flanders et al. (2017), Zi = Xi+1 can be used as a negative control exposure; in addition, we use Wi = Yi−1 as a negative control outcome, which satisfies Zi (Wi, Yi) | (Xi, Ui) and Wi Xi | Ui.”
Examples of L2-Complete and Boundedly-Complete Distributions (2011, unknown): “dHaultfoeuille (2011) provides su¢ cient conditions for bounded completeness for random vectors X and Z that satisfy (X) = (Z) + "; where Z and " are independent, (Z) is absolutely continuous with respect to (wrt) Lebesgue measure with full support RX ; and " is absolutely continuous wrt Lebesgue m”
Nonparametric identification and estimation of transformation models (2015, background): “1 if the conditional distribution of the endogenous regressor X given the instrument Z is complete (see, e.g., Darolles, Florens, and Renault, 2002; Blundell and Powell, 2003; Newey and Powell, 2003; Hall and Horowitz, 2005; Severini and Tripathi, 2006; d’Haultfoeuille, 2006).”
Nonlinear Panel Data Estimation via Quantile Regressions (2015, unknown): “So taking T = 4 we have: fY2,Y3,Y4|Y1,X (y2, y3, y4 | y1, x) = ∫ fY2|Y1,η,X (y2 | y1, η, x) fY3|Y2,η,X (y3 | y2, η, x) ×fY4|Y3,η,X (y4 | y3, η, x) fη|Y1,X (η | y1, x) dη, (25)”
NONPARAMETRIC IDENTIFICATION USING INSTRUMENTAL VARIABLES: SUFFICIENT CONDITIONS FOR COMPLETENESS (2011, background): “3 of D’Haultfoeuille (2011). With the complete sequences explicitly specified in Lemma 1 and 2, we are ready to extend the completeness to a more general conditional density f(x|z).”
Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework (2015, unknown): “Lastly, we specify ξi = q(ai1, ηi1, agei1, ωi), with ωi uniform on (0, 1), using a series quantile modeling as in (25).”