Instrumental quantile regression inference for structural and treatment effect models

By Victor Chernozhukov, Christian Hansen. 2006

654 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4bad888e5326fd17706cae953945a35ccf56828b

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Citations in the corpus

The trade-growth nexus in the developing countries: a quantile regression approach (2010, neutral): “12 See e.g., Abadie et al. (2002), Honore ´ and Hu (2004), Chernozhukov and Hansen (2005, 2006) ,”
The changes-in-changes model with covariates (2015, background): “3See for instance the discussion in Chernozhukov and Hansen (2005).”
SMOOTHED ESTIMATING EQUATIONS FOR INSTRUMENTAL VARIABLES QUANTILE REGRESSION (2016, methodology): “Within this framework, Chernozhukov and Hansen (2006) and Chernozhukov et al. (2009) develop estimation and inference procedures under strong identification.”
Banking risk and regulation: Does one size fit all? (2012, methodology): “We estimate the quantile regressions using the methodology proposed by Chernozhukov and Hansen (2006, 2008) and Galvao (2009) by including also the lagged regressors as instruments to reduce the bias associated with dynamic quantile regressions.”
Censored regression quantiles with endogenous regressors (2007, background): “…restriction figures prominently in recent results on the estimation of coefficients for linear models with endogenous regressors, including those by Chernozhukov and Hansen (2005, 2006) and Sawata (2005); however, as Hong and Tamer (2003) demonstrate, identification of b0 under this weaker…”
Estimation of counterfactual distributions using quantile regression (2006, background): “5 Abadie, Angrist and Imbens (2002), Chesher (2003), Chernozhukov and Hansen (2006), for instance, have proposed IV estimators for conditional quantile functions.”
Trade as an engine of creative destruction: Mexican experience with Chinese competition (2013, methodology): “Figure 2 displays this 20For the implementation of the quantile IV regressions we use the strategy and codes developed by Chernozhukov and Hansen (2006).”
Quantile Selection Models with an Application to Understanding Changes in Wage Inequality (2017, neutral): “[24] Chernozhukov, V.”
Nonparametric Instrumental Variables Estimation of a Quantile Regression Model (2006, methodology): “Chernozhukov and Hansen (2005a) , and Ma and Koenker (2006).”
Bayesian quantile regression methods (2010, background): “Following Chernozhukov and Hansen (2006) consider the model Y = D′α(U) + X ′β(U), U |X, Z ∼ Uniform(0, 1) in which D is statistically dependent on U, D′α(τ) + X ′β(τ) is strictly increasing in τ, and Z is a set of instrumental variables that are independent of U but statistically dependent on D.”
A Test of the Conditional Independence Assumption in Sample Selection Models: A TEST OF THE CONDITIONAL INDEPENDENCE ASSUMPTION (2012, neutral): “Since sums and products of Donsker functions are Donsker, this veri es Assumption I.4 (a) of Chernozhukov and Hansen (2006).11 Assumption I.4 (b) of Chernozhukov and Hansen (2006) holds by our Assumptions 1 to 3 that imply that x02i 0 + h (x 0 1i 0) is the conditional quantile of yi given x1i and…”
Conditional Inference With a Functional Nuisance Parameter (2014, neutral): “Both Chernozhukov and Hansen (2008) and Jun (2008) suggested Anderson-Rubin type statistics for this model which have stable power, but which are ine cient in overidenti ed models under strong identi cation.”
Estimating and Testing a Quantile Regression Model with Interactive Effects (2012, neutral): “This leads to a chi-square property for γ̂(τ )A(τ )γ̂(τ ) (Chernozhukov and Hansen, 2006).”