A neural network demand system with heteroskedastic errors

Michael McAleer, Marcelo C. Medeiros, Daniel Slottje

Research output: Contribution to journalArticlepeer-review


In this paper we consider estimation of demand systems with flexible functional forms, allowing an error term with a general conditional heteroskedasticity function that depends on observed covariates, such as demographic variables. We propose a general model that can be estimated either by quasi-maximum likelihood (in the case of exogenous regressors) or generalized method of moments (GMM) if the covariates are endogenous. The specification proposed in the paper nests several demand functions in the literature and the results can be applied to the recently proposed Exact Affine Stone Index (EASI) demand system of [Lewbel, A., Pendakur, K., 2008. Tricks with Hicks: The EASI implicit Marshallian demand system for unobserved heterogeneity and flexible Engel curves. American Economic Review (in press)]. Furthermore, flexible nonlinear expenditure elasticities can be estimated.

Original languageEnglish (US)
Pages (from-to)359-371
Number of pages13
JournalJournal of Econometrics
Issue number2
StatePublished - Dec 2008
Externally publishedYes


  • Asymptotic theory
  • Demand functions
  • Engel curves
  • Estimating demand systems
  • Exact affine Stone index (EASI)
  • Flexible forms
  • Heteroskedasticity
  • Neural networks

ASJC Scopus subject areas

  • Economics and Econometrics


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