Point-identification in multivariate nonseparable triangular models

TL;DR

This paper establishes a general nonparametric point-identification framework for multivariate nonseparable triangular models, extending identification results to models with endogenous variables and multivariate heterogeneity.

Contribution

It introduces a novel nonparametric identification method for complex multivariate models, including Hedonic and BLP models, without requiring exogeneity or index restrictions.

Findings

Point-identification for multivariate nonseparable triangular models

Identification of Hedonic models with endogenous characteristics

Identification of BLP model without index restrictions

Abstract

In this article we introduce a general nonparametric point-identification result for nonseparable triangular models with a multivariate first- and second stage. Based on this we prove point-identification of Hedonic models with multivariate heterogeneity and endogenous observable characteristics, extending and complementing identification results from the literature which all require exogeneity. As an additional application of our theoretical result, we show that the BLP model (Berry et al. 1995) can also be identified without index restrictions.