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Spatial Differencing for Sample Selection Models with Unobserved Heterogeneity

Klein, Axel and Tchuente, Guy (2020) Spatial Differencing for Sample Selection Models with Unobserved Heterogeneity. Discussion paper. arXiv (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:84482)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://arxiv.org/abs/2009.06570

Abstract

This paper derives identification, estimation, and inference results using spatial differencing in sample selection models with unobserved heterogeneity. We show that under the assumption of smooth changes across space of the unobserved sub-location specific heterogeneities and inverse Mills ratio, key parameters of a sample selection model are identified. The smoothness of the sub-location specific heterogeneities implies a correlation in the outcomes. We assume that the correlation is restricted within a location or cluster and derive asymptotic results showing that as the number of independent clusters increases, the estimators are consistent and asymptotically normal. We also propose a formula for standard error estimation. A Monte-Carlo experiment illustrates the small sample properties of our estimator. The application of our procedure to estimate the determinants of the municipality tax rate in Finland shows the importance of accounting for unobserved heterogeneity.

Item Type: Monograph (Discussion paper)
Divisions: Divisions > Division of Human and Social Sciences > School of Economics
Depositing User: Guy Tchuente Nguembu
Date Deposited: 26 Nov 2020 20:00 UTC
Last Modified: 15 Sep 2021 15:23 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/84482 (The current URI for this page, for reference purposes)
Tchuente, Guy: https://orcid.org/0000-0001-8507-3337
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