Efficient Semiparametric Estimation of Quantile Treatment Effects

By Sergio Firpo. 2004

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, background): “(4.9) Such quantile treatment effects have been studied in Doksum (1974) and Lehman (1974), and more recently in Abadie, Angrist and Imbens (2002), Chernozhukov and Hansen (2005), Firpo (2007), Bitler, Gelbach and Hoynes (2002).”
Econometric Methods for Program Evaluation (2018, background): “…Hirano and Imbens, 2004; Imai and van Dyk, 2004; Cattaneo, 2010; Farrell, 2015; Yang et al., 2016), to quantiles and related treatment effects (Firpo, 2007; Cattaneo, 2010), and to the analysis and interpretation of counterfactual distributional treatment effects (DiNardo et al., 1996;…”
Estimation of Multivalued Treatment Effects under Conditional Independence (2013, methodology): “The IPW estimation procedure follows the work of Hirano et al. (2003) and Firpo (2007) for binary mean and quantile treatment effects, and thus extends the idea of inverse probability weighting to a multivalued treatment context (see also Imbens (2000)).”
Subsampling Inference on Quantile Regression Processes (2005, background): “(2002), Chernozhukov and Hansen (2005), Chesher (2003), and Firpo (2004) , that deal with other types of sample selectivity and endogeneity.”
Assessing the Impact of Crop Diversification on Farm Poverty in India (2015, methodology): “, Firpo, 2007; Hirano, Imbens, & Ridder, 2003; Imbens, 2004; Heckman, et al., 1998). Hirano and Imbens (2004) show that if assignment to treatment is weakly unconfounded, given the covariates X, then it is also weakly unconfounded given the GPS.”
Estimation of Conditional Average Treatment Effects With High-Dimensional Data (2019, methodology): “Such a counterfactual analysis requires accounting for the Oaxaca-Blinder decomposition of heterogeneous outcome distributions into structural heterogeneity (FY |X) and distributional heterogeneity (FX); see Fortin, Lemieux, and Firpo (2011) for a review.”
Testing Conditional Independence via Rosenblatt Transforms (2009, neutral): “First, let Fn be a sequence of classes of uniformly bounded functions φ(·, ·) : R×S → [0, 1], such that for each v ∈ S, φ(·, v) is monotone, and for each ε > 0, sup(y,v)∈R×Ssupη∈[0,ε] |φ(y, v + η)− φ(y, v − η)| < Mnε (22)”
Estimation of Quantile Treatment Effects with Stata (2010, methodology): “Assumption 4 identifies the unconditional QTE, as shown in Firpo (2007), Frölich (2007a), and Melly (2006). The estimator of Firpo (2007) is a special case of (4), when D is used as its own instrument.”
Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression (2015, background): “See for instance Chernozhukov and Hansen (2006, 2008) and Firpo (2007) .”
Decomposing Wage Distributions using Recentered Influence Function Regressions (2018, methodology): “The main contribution of the paper is to suggest a simple regressionbased procedure to remedy this shortcoming building on recent work by Firpo, Fortin, and Lemieux (2006).”
Efficient Estimation of the Dose–Response Function Under Ignorability Using Subclassification on the Covariates (2011, neutral): “(See, e.g., Firpo (2007).)”
Dynamic Quantile Models of Rational Behavior (2019, neutral): “QR has been largely used in program evaluation studies (Chernozhukov and Hansen (2005) and Firpo (2007)).”