A neural network demand system with heteroskedastic errors

Michael McAleer, Marcelo Medeiros () and Daniel Slottje

Journal of Econometrics, 2008, vol. 147, issue 2, 359-371

Abstract: 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.

Keywords: Demand; functions; Estimating; demand; systems; Flexible; forms; Exact; affine; Stone; index; (EASI); Neural; networks; Asymptotic; theory; Heteroskedasticity; Engel; curves (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:147:y:2008:i:2:p:359-371

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