Comparing Distribution and Quantile Regression

Samantha Leorato and Franco Peracchi

No 1511, EIEF Working Papers Series from Einaudi Institute for Economics and Finance (EIEF)

Abstract: We study the sampling properties of two alternative approaches to estimating the conditional distribution of a continuous outcome Y given a vector X of regressors. One approach – distribution regression – is based on direct estimation of the conditional distribution function; the other approach – quantile regression – is instead based on direct estimation of the conditional quantile function. Indirect estimates of the conditional quantile function and the conditional distribution function may then be obtained by inverting the direct estimates obtained from either approach or, to guarantee monotonicity, their rearranged versions. We provide a systematic comparison of the asymptotic and finite sample performance of monotonic estimators obtained from the two approaches, considering both cases when the underlying linear-in-parameter models are correctly specified and several types of model misspecification of considerable practical relevance.

Pages: 35
Date: 2015, Revised 2015-10
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.eief.it/files/2015/10/wp-11-comparing-d ... ntile-regression.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eie:wpaper:1511

Access Statistics for this paper

More papers in EIEF Working Papers Series from Einaudi Institute for Economics and Finance (EIEF) Contact information at EDIRC.
Bibliographic data for series maintained by Facundo Piguillem (facundo.piguillem@eief.it).