Non-parametric hypothesis testing procedures and applications to demand analysis
type: Item
instance of: scholarly article
title: Non-parametric hypothesis testing procedures and applications to demand analysis
zbMATH Open document ID: 0626.62045
DOI: 10.1016/0304-4076(85)90136-8
author: Larry G. Epstein
author: Adonis Yatchew
published in: Journal of Econometrics
publication date: 1985-01-01T00:00:00Z
review text: This paper proposes a hypothesis test that a (possibly vector-valued) regression function g lies in a particular family of functions \({\mathcal F}\), not necessarily a finite-dimensional parametric family, where \({\mathcal F}\) is a compact subset of an appropriate topological space of continuous functions. Under the assumption of independently and identically distributed disturbances with mean 0 and known covariance matrix \(\Sigma\), the test is shown to be consistent for a broad range of alternatives and an upper bound on the significance level is established. The application on which this paper is focused, is a non-parametric approach to demand- and factor analysis. In particular, the authors describe asymptotically valid and consistent non-parametric tests of utility maximization and utility maximization with homothetic utility.
Mathematics Subject Classification ID: 62G10
Mathematics Subject Classification ID: 62P20
zbMATH DE Number: 4018133
zbMATH Keywords: demand analysis
zbMATH Keywords: independently and identically distributed disturbances
zbMATH Keywords: known covariance matrix
zbMATH Keywords: consistent
zbMATH Keywords: upper bound on the significance level
zbMATH Keywords: tests of utility maximization
zbMATH Keywords: homothetic utility
reviewed by: R. Perez
MaRDI profile type: Publication
full work available at URL: 0304-4076(85)90136-8
OpenAlex ID: W2071135486
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description: scientific article; zbMATH DE number 4018133