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Testing multivariate economic restrictions using quantiles: the example of Slutsky negative semidefiniteness

Holger Dette, Stefan Hoderlein () and Natalie Neumeyer
Additional contact information
Holger Dette: Institute for Fiscal Studies
Stefan Hoderlein: Institute for Fiscal Studies and Boston College
Natalie Neumeyer: Institute for Fiscal Studies

No CWP14/11, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract:

This paper is concerned with testing rationality restrictions using quantile regression methods. Specifically, we consider negative semidefiniteness of the Slutsky matrix, arguably the core restriction implied by utility maximization. We consider a heterogeneous population characterized by a system of nonseparable structural equations with infinite dimensional unobservable. To analyze the economic restriction, we employ quantile regression methods because they allow us to utilize the entire distribution of the data. Difficulties arise because the restriction involves several equations, while the quantile is a univariate concept. We establish that we may test the economic restriction by considering quantiles of linear combinations of the dependent variable. For this hypothesis we develop a new empirical process based test that applies kernel quantile estimators, and derive its large sample behavior. We investigate the performance of the test in a simulation study. Finally, we apply all concepts to Canadian individual data, and show that rationality is an acceptable description of actual individual behavior.

Date: 2011-05-01
New Economics Papers: this item is included in nep-ecm
References: View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Related works:
Journal Article: Testing multivariate economic restrictions using quantiles: The example of Slutsky negative semidefiniteness (2016) Downloads
Working Paper: Testing Multivariate Economic Restrictions Using Quantiles: The Example of Slutsky Negative Semidefiniteness (2013) Downloads
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