Instrumental variables estimators of nonparametric models with discrete endogenous regressors

By M. Das. 2005

131 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6a70f2e65cd4a835160d26f09b5a145bb0eeff77

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In Statistics and Probability

Citations in the corpus

Identification and estimation of local average derivatives in non-separable models without monotonicity (2009, unknown): “mfi(x) i Z mfl(x)g(fl)dfl ‚2 g(fi)dfiw(x)dx = 0; ( 12 )”
Identification and Estimation of Regression Models with Misclassification (2006, unknown): “It is analogous to the requirement in IV models that the instrument be related to the endogenous regressor conditional on all the other regressors (although obviously the analogy is imperfect since x∗ is unobserved) and is like the identification assumption in Theorem 2.1 of Das (2004).”
Estimation of Average Treatment Effects With Misclassification (2007, background): “Equation (5) is new, though both Mahajan (2005) and Das (2004) provide closely related results as discussed in the introduction.”
ON THE COMPLETENESS CONDITION IN NONPARAMETRIC INSTRUMENTAL PROBLEMS (2010, background): “When X0 takes more than two values, it can be shown that the completeness condition is equivalent to the positivity of a variance matrix (see Das, 2005, Thm. 2.1).”
An IV Model of Quantile Treatment Effects (2002, background): “These conditions are akin to the identification of average treatment effects in Abadie (2001) or Das (2001) .”
Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates (2009, result): “Under the assumptions given in the Appendix, we can prove [Fan and Gijbels (1996), pages 101–103 or Carroll et al.”
Trending time-varying coefficient time series models with serially correlated errors (2007, background): “If some of Xi are endogenous variables, the various instrumentalvariable-type estimates of linear and nonlinear simultaneous equations and transformation models can be easily applied here with some modifications; see Cai et al. (2005) and Das (2005) .”
IDENTIFICATION AND ESTIMATION BY PENALIZATION IN NONPARAMETRIC INSTRUMENTAL REGRESSION (2010, background): “The above setting is the core of many econometric studies; see, e.g., Darolles et al. (2002), Newey and Powell (2003), Matzkin (2003), Chernozhukov and Hansen (2005), Das (2005), or Hall and Horowitz (2005), to name but a few.”
Identification and Estimation of Triangular Simultaneous Equations Models without Additivity (2002, background): “Das (2001) also allows for nonseparable disturbances, but considers a single index setting with monotonicity. The independence of disturbances and instruments that we impose is stronger than the conditional mean restriction of Newey and Powell (2003), Das (2004), Darrolles, Florens, and Renault (200”
Local Linear GMM Estimation of Functional Coefficient IV Models With an Application to Estimating the Rate of Return to Schooling (2013, background): “Recently, Das (2005), Cai et al. (2006, CDXW hereafter), Lewbel (2007), Cai and Li (2008), Tran and Tsionas (2010), and Su (2012), among others, had considered the semiparametric models with endogenous variables and they suggested a nonparametric/semiparametric generalized method of moments (GMM)…”
Coherency and Completeness of Structural Models Containing a Dummy Endogenous Variable (2007, unknown): “(5) y1 = I [h(y1, y2, w)+ e1 ≥ 0] for some function h, where e1 ∈ w.”