Identification of a class of index models: A topological approach

We establish nonparametric identification in a class of so-called index models using a novel approach that relies on general topological results. Our proof strategy imposes very weak smoothness conditions on the functions to be identified and does not require any large support conditions on the regressors in our model. We apply the general identification result to additive random utility and competing risk models.

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Year of publication:	2019
Authors:	Fosgerau, Mogens ; Kristensen, Dennis
Publisher:	London : Centre for Microdata Methods and Practice (cemmap)
Subject:	nonparametric identification \| discrete choice \| competing risks \| index

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Series:	cemmap working paper ; CWP52/19
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	10.1920/wp.cem.2019.5219 [DOI] 1679096621 [GVK] hdl:10419/211145 [Handle] RePEc:ifs:cemmap:52/19 [RePEc]
Classification:	C14 - Semiparametric and Nonparametric Methods ; C35 - Discrete Regression and Qualitative Choice Models ; c36 ; C41 - Duration Analysis
Source:	EconStor

Persistent link: https://www.econbiz.de/10012146404