Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors

By Joseph G. Altonji, Rosa L. Matzkin. 2005

350 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, background): “When Y = Y (D) for a potential outcome process Y (d) that is independent of treatment D conditional on the covariates Z and differentiable in d, it was shown by Altonji and Matzkin (2005) and Florens et al. (2008) that T (d) = E[∂dY (D)”
Nonlinear Policy Rules and the Identification and Estimation of Causal Effects in a Generalized Regression Kink Design (2012, methodology): “…randomized relationship between B and V , the trivial integration over the (degenerate) distribution of V conditional on B = b0 will imply that T Tb0|0 = T Tb0 ≡ E [y1 (b0,V,U) |B = b0], which is literally the T T parameter discussed in Florens et al. (2008) and LAR in Altonji and Matzkin (2005).”
Nonparametric Identification in Nonseparable Panel Data Models with Generalized Fixed Effects (2009, background): “Altonji and Matzkin (2005) treat the model under an exchangeability assumption that is different from ours and more closely related to random effects.”
Control functions in nonseparable simultaneous equations models (2014, background): “…be derived from the distribution of (Y1 Y2 X) as G(y2) = ∫ E(Y1|Y2 = y2 T = t)fT (t)dt where T = FY2|X(Y2) The local average response function, defined by Altonji and Matzkin (2005) as β(y2) = ∫ ∂m1(y2 ε1) ∂y2 fε1|Y2=y2(ε1)dε1 can be derived from the distribution of (Y1 Y2 X) as β(y2) =…”
Local indirect least squares and average marginal effects in nonseparable structural systems (2012, methodology): “An e¤ective way to recover and estimate well-de ned e¤ects of interest in such circumstances is to use control variables (see, e.g., Altonji and Matzkin, 2005; Hoderlein and Mammen, 2007; Hoderlein, 2005, 2007; and Imbens and Newey, 2009).”
Average and Quantile Effects in Nonseparable Panel Models (2009, background): “Chamberlain (1980, 1984), Altonji and Matzkin (2005), Bester and Hansen (2008), and others have used control functions for panel data estimation.”
Identification in Nonparametric Simultaneous Equations Models (2008, methodology): “Altonji and Matzkin (2001, 2005) provided estimators for average derivatives in nonseparable models, using conditional independence, and for nonparametric nonseparable functions, using exchangeability.”
Estimating Panel Data Models in the Presence of Endogeneity and Selection (2010, methodology): “Semiparametric and nonparametric procedures that relax the separability of the unobserved effect from the effects of other variables in binary response panel data models (see, for example, Altonji and Matzkin, 2005), can add to the flexibility of the approach.”
The Effects of Birth Inputs on Birthweight (2008, methodology): “A nonparametric approach to this problem, along the lines of Altonji and Matzkin (2005), may be feasible.”
Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments (2007, methodology): “Imbens and Newey (2002) consider identification and estimation of the average structural function in a nonparametric model using the control variate approach, building on the work of Blundell and Powell (2004) and Altonji and Matzkin (2005).”