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INFERENCE ON COUNTERFACTUAL DISTRIBUTIONS
by
Melly, Blaise
, Fernández-Val, Iván
, Chernozhukov, Victor
in
Analytical estimating
/ Bootstrap mechanism
/ Bootstrap method
/ Causal inference
/ Central limit theorem
/ Changes
/ Contrafactuals
/ Counterfactual distribution
/ Counterfactual thinking
/ decomposition analysis
/ Distribution
/ distribution regression
/ Dominance
/ duration/transformation regression
/ Econometrics
/ Economic models
/ Economic theory
/ Estimation methods
/ Estimators
/ exchangeable bootstrap
/ Hadamard differentiability of the counterfactual operator
/ Inference
/ Minimum wage
/ Modelling
/ Policy analysis
/ Quantile regression
/ Regression analysis
/ Statistical inference
/ Stochastic processes
/ Studies
/ U.S.A
/ unconditional quantile and distribution effects
2013
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INFERENCE ON COUNTERFACTUAL DISTRIBUTIONS
by
Melly, Blaise
, Fernández-Val, Iván
, Chernozhukov, Victor
in
Analytical estimating
/ Bootstrap mechanism
/ Bootstrap method
/ Causal inference
/ Central limit theorem
/ Changes
/ Contrafactuals
/ Counterfactual distribution
/ Counterfactual thinking
/ decomposition analysis
/ Distribution
/ distribution regression
/ Dominance
/ duration/transformation regression
/ Econometrics
/ Economic models
/ Economic theory
/ Estimation methods
/ Estimators
/ exchangeable bootstrap
/ Hadamard differentiability of the counterfactual operator
/ Inference
/ Minimum wage
/ Modelling
/ Policy analysis
/ Quantile regression
/ Regression analysis
/ Statistical inference
/ Stochastic processes
/ Studies
/ U.S.A
/ unconditional quantile and distribution effects
2013
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Do you wish to request the book?
INFERENCE ON COUNTERFACTUAL DISTRIBUTIONS
by
Melly, Blaise
, Fernández-Val, Iván
, Chernozhukov, Victor
in
Analytical estimating
/ Bootstrap mechanism
/ Bootstrap method
/ Causal inference
/ Central limit theorem
/ Changes
/ Contrafactuals
/ Counterfactual distribution
/ Counterfactual thinking
/ decomposition analysis
/ Distribution
/ distribution regression
/ Dominance
/ duration/transformation regression
/ Econometrics
/ Economic models
/ Economic theory
/ Estimation methods
/ Estimators
/ exchangeable bootstrap
/ Hadamard differentiability of the counterfactual operator
/ Inference
/ Minimum wage
/ Modelling
/ Policy analysis
/ Quantile regression
/ Regression analysis
/ Statistical inference
/ Stochastic processes
/ Studies
/ U.S.A
/ unconditional quantile and distribution effects
2013
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Journal Article
INFERENCE ON COUNTERFACTUAL DISTRIBUTIONS
2013
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Overview
Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article, we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates. For either of these scenarios, we derive joint functional central limit theorems and bootstrap validity results for regression-based estimators of the status quo and counterfactual outcome distributions. These results allow us to construct simultaneous confidence sets for function-valued effects of the counterfactual changes, including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals. These confidence sets can be used to test functional hypotheses such as no-effect, positive effect, or stochastic dominance. Our theory applies to general counterfactual changes and covers the main regression methods including classical, quantile, duration, and distribution regressions. We illustrate the results with an empirical application to wage decompositions using data for the United States. As a part of developing the main results, we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the entire conditional distribution. We show that distribution regression encompasses the Cox duration regression and represents a useful alternative to quantile regression. We establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals.
Publisher
Econometric Society,Blackwell Publishing Ltd
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