Unconditional Quantile Partial Effects via Conditional Quantile Regression

Javier Alejo, Antonio Galvao, Julián Martinez-Iriarte and Gabriel Montes-Rojas ()
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Julián Martinez-Iriarte: UC Santa Cruz

No 217, Working Papers from Red Nacional de Investigadores en Economía (RedNIE)

Abstract: This paper develops a semi-parametric procedure for estimation of unconditional quantile partial effects using quantile regression coefficients. The main result is based on the fact that, for continuous covariates, unconditional quantile effects are a weighted average of conditional ones at particular quantile levels that depend on the covariates. We propose a two-step estimator for the unconditional effects where in the first step one estimates a structural quantile regression model, and in the second step a non-parametric regression is applied to the first step coefficients. We establish the asymptotic properties of the estimator, say consistency and asymptotic normality. Monte Carlo simulations show numerical evidence that the estimator has very good finite sample performance and is robust to the selection of bandwidth and kernel. To illustrate the proposed method, we study the canonical application of the Engel’s curve, i.e. food expenditures as a share of income.

Keywords: Quantile regression; unconditional quantile regression; nonparametric regression (search for similar items in EconPapers)
JEL-codes: C14 C21 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2023-02
New Economics Papers: this item is included in nep-dcm
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https://rednie.eco.unc.edu.ar/files/DT/217.pdf (application/pdf)

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Working Paper: Unconditional Quantile Partial Effects via Conditional Quantile Regression (2023)
Working Paper: Unconditional Quantile Partial Effects via Conditional Quantile Regression (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:aoz:wpaper:217

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