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Nonparametric structural analysis of discrete data: the quantile-based control function approach

Lee, J.; (2010) Nonparametric structural analysis of discrete data: the quantile-based control function approach. Doctoral thesis, UCL (University College London). Green open access

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Abstract

The first chapter is introduction and Chapter 2 proposes formal frameworks for identifiability and testability of structural features allowing for set identification. The results in Chapter 2 are used in other chapters. The second section of Chapter 3, Chapter 4 and Chapter 5 contain new results. Chapter 3 has two sections. The first section introduces the quantile-based control function approach (QCFA) proposed by Chesher (2003) to compare and contrast other results in Chapter 4 and 5. The second section contains new findings on the local endogeneity bias and testability of endogeneity. Chapter 4 assumes that the structural relations are differentiable and applies the QCFA to several models for discrete outcomes. Chapter 4 reports point identification results of partial derivatives with respect to a continuously varying endogenous variable. Chapter 5 relaxes differentiability assumptions and apply the QCFA with an ordered discrete endogeneous variable. The model in Chapter 5 set identifies partial differences of a nonseparable structural function.

Type:Thesis (Doctoral)
Title:Nonparametric structural analysis of discrete data: the quantile-based control function approach
Open access status:An open access version is available from UCL Discovery
Language:English
UCL classification:UCL > School of Arts and Social Sciences > Faculty of Social and Historical Sciences > Economics

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