Conditional quantile estimation and inference for arch models

Roger Koenker, Quanshui Zhao

Research output: Contribution to journalArticlepeer-review


Quantile regression methods are suggested for a class of ARCH models. Because conditional quantiles are readily interpretable in semiparametric ARCH models and are inherently easier to estimate robustly than population moments, they offer some advantages over more familiar methods based on Gaussian like-lihoods. Related inference methods, including the construction of prediction intervals, are also briefly discussed.

Original languageEnglish (US)
Pages (from-to)793-813
Number of pages21
JournalEconometric Theory
Issue number5
StatePublished - 1996

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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