Antonio F. Galvao, Gabriel Montes-Rojas
arXiv 21 Aug 2025 · Econometrics
arXiv:2508.15749 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a new framework for multivariate quantile regression based on the multivariate distribution function, termed multivariate quantile regression (MQR). In contrast to existing approaches--such as directional quantiles, vector quantile regression, or copula-based methods--MQR defines quantiles through the conditional probability structure of the joint conditional distribution function. The method constructs multivariate quantile curves using sequential univariate quantile regressions derived from conditioning mechanisms, allowing for an intuitive interpretation and flexible estimation of marginal effects. The paper develops theoretical foundations of MQR, including asymptotic properties of the estimators. Through simulation exercises, the estimator demonstrates robust finite sample performance across different dependence structures. As an empirical application, the MQR framework is applied to the analysis of exchange rate pass-through in Argentina from 2004 to 2024.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Antonio Galvao and Kengo Kato and Gabriel Montes-Rojas and José Olmo (2014) Testing linearity against threshold effects: uniform inference in quantile regression self | 0.737 | 5 | 2 | 60% |
| 2 | Luciano de Castro and Antonio F. Galvao and David M. Kaplan and Xin… (2019) Smoothed GMM for quantile models self | 0.644 | 3 | 2 | 67% |
| 3 | Guillaume Carlier and Victor Chernozhukov and Alfred Galichon (2016) Vector quantile regression | 0.644 | 2 | 2 | 100% |
| 4 | Guillaume Carlier and Victor Chernozhukov and Alfred Galichon (2017) Vector quantile regression beyond the specified case | 0.644 | 2 | 2 | 100% |
| 5 | Victor Chernozhukov and Alfred Galichon and Mark Hallin and M. Henry (2015) Monge-Kantorovich depth, ranks, quantiles, and signs | 0.644 | 2 | 2 | 100% |
| 6 | M. Hallin and D. Paindaveine and M. Siman (2010) Multivariate quantiles and multiple-output regression quantiles: From $L_1$ optimization to halfspace depth | 0.644 | 2 | 2 | 100% |
| 7 | G. Montes-Rojas (2019) Quantile impulse response functions self | 0.644 | 2 | 2 | 100% |
| 8 | Liqiong Chen and Antonio F. Galvao and Suyong Song (2021) Quantile regression with generated regressors self | 0.511 | 3 | 2 | 33% |
| 9 | Linjie Ma and Roger Koenker (2006) Quantile regression methods for recursive structural equation models | 0.511 | 3 | 2 | 33% |
| 10 | Joel Horowitz (1998) Bootstrap Methods for Median Regression Models | 0.511 | 2 | 1 | 100% |
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