Hierarchical spline models for conditional quantiles and the demand for electricity

Wallace Hendricks, Roger Koenker

Research output: Contribution to journalArticlepeer-review


Methods for estimating nonparametric models for conditional quantiles are suggested based on the regression quantile methods of Koenker and Bassett. Spline parameterizations of the conditional quantile functions are used. The methods are illustrated by estimating hierarchical models for household electricity demand using data from the Chicago metropolitan area. The empirical results show that lower quantiles of demand (“base-load”) vary only slightly across residential households. This variability is difficult to explain using household characteristics. However, upper quantiles of the demand distribution vary considerably and are systematically related to household characteristics and appliance ownership. The implications of analyzing mean demand behavior rather than various quantiles of the distribution of demand are also discussed.

Original languageEnglish (US)
Pages (from-to)58-68
Number of pages11
JournalJournal of the American Statistical Association
Issue number417
StatePublished - Mar 1992


  • Hierarchical models
  • Nonparametric regression
  • Regression quantiles
  • Splines

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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