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Essays on Dynamic Unobservable Heterogeneity

Sarpietro, Silvia; (2021) Essays on Dynamic Unobservable Heterogeneity. Doctoral thesis (Ph.D), UCL (University College London).

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A large body of the recent literature has highlighted the importance of unobservable heterogeneity and its dynamics for many questions in Economics. In this thesis, I study the interplay between cross-sectional heterogeneity and dynamics with micro panels, i.e., panel data with many units (N) observed over a relatively smaller number of periods (T). I focus on how to estimate dynamic unobservable heterogeneity and exploit this for the problem of forecasting individual outcomes. The second chapter, titled “Dynamic Unobservable Heterogeneity: Income Inequality and Job Polarization”, studies how to use state-space methods to estimate unobserved heterogeneity and its dynamics when using micro panels. I illustrate the methodology with an empirical application to earnings dynamics and job polarization using a novel dataset for the UK. The third chapter, titled “Individual Forecast Selection”, continues with an analysis of unobserved heterogeneity for forecasting with panel data. It proposes a new methodology for forecasting that relies on individual forecast selection. For each individual, the approach selects the best forecast out of a class of competing methods, based on the out-of-sample accuracy of the method in one past time period. It is shown that this approach can be minimax-regret optimal relative to choosing the same forecasting model for everyone. Finally, the last chapter, titled “Regularized CUE: a Quasi-Likelihood Approach”, discusses some GMM-type estimators used in panel data and a proposed modification to the Continuous Updating Estimator (CUE). Analytical results and Monte Carlo simulations show that this modification has nice finite sample properties: It reduces the finite sample variance of the CUE, restoring its finite sample moments.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Essays on Dynamic Unobservable Heterogeneity
Event: UCL (University College London)
Language: English
Additional information: Copyright © The Author 2021. 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/10135005
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