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Open AccessArticle

Counterfactual Distributions in Bivariate Models—A Conditional Quantile Approach

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Center for Distributive, Labor and Social Studies (CEDLAS) and CONICET, Facultad de Ciencias Económicas, Universidad Nacional de La Plata (UNLP), La Plata 1900, Argentina
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Center for Distributive, Labor and Social Studies (CEDLAS), Facultad de Ciencias Económicas, Universidad Nacional de La Plata (UNLP), La Plata 1900, Argentina
*
Author to whom correspondence should be addressed.
A previous version [1] in Spanish was published in the Annals of the XLIX Annual Meeting of the AAEP.
Academic Editor: Gabriel Montes-Rojas
Econometrics 2015, 3(4), 719-732; https://doi.org/10.3390/econometrics3040719
Received: 10 September 2015 / Revised: 18 October 2015 / Accepted: 21 October 2015 / Published: 9 November 2015
(This article belongs to the Special Issue Quantile Methods)
This paper proposes a methodology to incorporate bivariate models in numerical computations of counterfactual distributions. The proposal is to extend the works of Machado and Mata (2005) and Melly (2005) using the grid method to generate pairs of random variables. This contribution allows incorporating the effect of intra-household decision making in counterfactual decompositions of changes in income distribution. An application using data from five latin american countries shows that this approach substantially improves the goodness of fit to the empirical distribution. However, the exercise of decomposition is less conclusive about the performance of the method, which essentially depends on the sample size and the accuracy of the regression model. View Full-Text
Keywords: counterfactual distributions; quantile regression; numeric integration; grid method; labor market; income distribution counterfactual distributions; quantile regression; numeric integration; grid method; labor market; income distribution
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Alejo, J.; Badaracco, N. Counterfactual Distributions in Bivariate Models—A Conditional Quantile Approach. Econometrics 2015, 3, 719-732.

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