Instrumental variable quantile regression: A robust inference approach

By Victor Chernozhukov, Christian Hansen. 2008

568 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f57b1079018b7107b55517791fd3205421b91665

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Citations in the corpus

Penalized Quantile Regression for Dynamic Panel Data (2010, methodology): “Consistency is a simple application of Theorem 2 of Koenker (2004), which shows consistency of the PQRFE estimator under assumptions (A1)–(A5), together with the IV proof in Chernozhukov and Hansen (2006). Since we are assuming identification of the true parameters with the IV, consistency follows.”
Unconditional Quantile Regression for Panel Data with Exogenous or Endogenous Regressors (2010, unknown): “To adopt similar terminology as Chernozhukov and Hansen [2008], the Structural 1u∗ is simply the CDF of α + : uit = P (α + ≤ αi + it).”
Instrumental Variable Quantile Estimation of Spatial Autoregressive Models (2007, methodology): “An important development in the literature of quantile regression is to allow endogeneity in the model and to introduce IV technique to handle the endogeneity (Chernozhukov and Hansen, 2005, 2006, 2008).”
Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems (2013, methodology): “Step 3 constructs an estimator α̌ of the coefficient α0 via an instrumental LAD regression proposed in [12], using (v̂i) n i=1 as instruments.”
Quantiles via moments (2019, methodology): “…it very easy to implement quantile regression in a wider class of models.5 In particular, we will explore the use of (MC1) in the estimation of panel data models with fixed-effects, and (MC2) in the estimation of structural quantile functions as defined by Chernozhukov and Hansen (2006, 2008).”
Systemic Risk and the Macroeconomy: An Empirical Evaluation (2016, unknown): “19Examples of instrumental variables approaches include Abadie, Angrist and Imbens (2002), Chernozhukov and Hansen (2008), and Schennach (2008). Examples of bias correction methods include He and Liang (2000), Chesher (2001), and Wei and Carroll (2009).”
A quantile regression approach for estimating panel data models using instrumental variables (2009, methodology): “…1 2NThNT XN i=1 XT t=1 I j û it τð Þ j V hNT ΨitΦitV withΨit=(wit′ ,xit′ ,zit′)′,Φit=(dit′ ,xit′ ,zit′)′, ûit(τ)=yit−dit′δ (̂τ)−xit′ β̂(τ)− zit′ α̂(τ) and h is a properly chosen bandwidth (see Koenker (2005) and Chernozhukov and Hansen (2008) for additional details including specific choices of…”
Pension Wealth and Household Savings in Europe: Evidence from SHARELIFE (2013, unknown): “In his seminal article, Feldstein (1974) uses aggregate time-series data for the US and shows that a 1 dollar in increase in Social Security Wealth (SSW) depresses private saving by about 40 dollar cents. However, Feldstein and Liebman (2002) point out that this estimate of the displacement effect m”
SMOOTHED ESTIMATING EQUATIONS FOR INSTRUMENTAL VARIABLES QUANTILE REGRESSION (2016, methodology): “For inference procedures that are robust to weak identification, see Chernozhukov and Hansen (2008) and Jun (2008), for example.”
Unconditional Quantile Treatment Effects Under Endogeneity (2007, unknown): “Therefore, AAI (Abadie, Angrist, and Imbens 2002) and Chernozhukov and Hansen (2005, 2006, 2008) had proposed linear IV quantile regression estimators.”
A Simple Approach to Quantile Regression for Panel Data (2011, methodology): “This type of representation has been extensively used in the literature (e.g. Chernozhukov and Hansen, 2006, 2008).”