Decomposition of differences in distribution using quantile regression

By Blaise Melly. 2005

515 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fdabae2aee4240af81a1c1808d9576fe77ea419f

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Citations in the corpus

The Gender Gap between Earnings Distributions (2019, methodology): “(25)As noted in Melly and Huber (2011), “allowing for arbitrary heterogeneity and nonseparability” only identifies the bounds of the effects which are “usually very wide in typical applications”.”
Pricing determinants in the hotel industry: Quantile regression analysis (2010, background): “initially and primarily used to study wage distribution, wage inequality and wage determinants (Buchinsky, 1998; Bedard, 2003; Skoufias, 2003; Min and Kim, 2003; Martins and Pereira, 2004; Sakellariou, 2006; Melly, 2005).”
Instrumental Variable Quantile Regression (2004, background): “This discussion suggests that given estimators of the parameters α0(τ) and β0(τ) and the distribution of covariates FX(x), unconditional QTE can be estimated based on the plug-in principle; see for instance Machado and Mata (2005), Melly (2005) or Chernozhukov et al. (2013a).”
Happiness Decomposition: Female Optimism (2016, methodology): “Recently, it has been extended to address decomposition in other points of the distribution by the use of quantile regressions (see for instance Albrecht et al 2003, Machado and Mata 2005 and Melly 2005).”
Do remittances improve income inequality? An instrumental variable quantile analysis of the Kenyan case (2016, background): “In these graphs, the bars represent the counts of the number of households receiving that level of remittances, whereas 7 See, for example, Buchinsky (1994a, 1994b); Martins and Pereira (2004a, 2004b), and Melly (2005).”
Nonparametric estimation of distributional policy effects (2010, unknown): “Stock (1989) and Imbens and Newey (2006) consider estimation of policy effects on the mean of the outcome variable in a nonparametric framework.”
Semiparametric Counterfactual Density Estimation (2021, background): “…study the entire counterfactual distribution [Abadie, 2002, Chernozhukov and Hansen, 2005, Chernozhukov et al., 2013, Díaz, 2017, Firpo, 2007, Fortin et al., 2011, Frölich and Melly, 2013, Machado and Mata, 2005, Melly, 2005, Rothe, 2010, Wang et al., 2018, Wang and Qin, 2010, Zhang et al., 2012].”
Estimation of counterfactual distributions using quantile regression (2006, methodology): “residuals. This decomposition was developed and applied independently by Melly (2005b)”
The influence function of semiparametric estimators (2015, methodology): “In this section, we derive the influence function for effects that depend on the probability limit of a nonparametric instrumental variables (NPIV) estimator like those in Newey and Powell (2003), Newey (1991), and Ai and Chen (2003).”
Unconditional Quantile Regressions (2007, unknown): “See also Albrecht, Björklund and Vroman (2003), Gardeazabal and Ugidos (2005), and Melly (2005) for related attempts at performing Oaxaca-Blinder type decompositions of unconditional quantiles using conditional quantile regressions.”
Misspecification Testing in a Class of Conditional Distributional Models (2013, unknown): “This lemma is a minor generalization of Lemma 13 in Chernozhukov, FernandezVal, and Melly (2009). Lemma 2. Let either the null hypothesis or a fixed alternative, and Assumptions 1-6 be true. Define the empirical processes ν(y, x) = √ n(Ĥn(y, x) −H(y, x)) and ν0(y, x) = √ n(Ĥ n(y, x)−H̄(y, x)). Then ”