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Essays in econometrics

D'Adamo, Riccardo; (2023) Essays in econometrics. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

This thesis presents new methodologies in the field of Econometrics and their application to microeconomic data. In Chapter 2, I develop a framework for estimation of optimal individualized treatment rules in the presence of partial identification. I propose an estimation procedure that ensures Neyman-orthogonality with respect to nuisance components and provide statistical guarantees for its performance. The approach is illustrated using data from the Job Partnership Training Act Study to estimate the optimal participation of workers in a job training programme. Chapter 3 presents a new instrumental variable (IV) estimator for nonlinear models with endogenous covariates. This estimator formalizes the idea that the IVs should be “excluded variables” that have no direct explanatory power for the outcome, and does not require to specify the distribution of the endogenous covariates. The theoretical properties are explored through asymptotic theory and Monte Carlo simulations, and the method is illustrated with two empirical applications. Chapter 4 develops inference methods for linear regression models with many controls and clustering. I show that commonly used cluster-robust standard errors are inconsistent when the number of controls grows proportionally with the sample size. I then propose a new standard error formula that allows to carry out valid inference in high-dimensional regression models. Monte Carlo evidence supports the theoretical results and the proposed method is illustrated with an empirical application that studies the impact of abortion on crime.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Essays in econometrics
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10174284
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