Posterior average effects

Arbeitspapier

Economists are often interested in estimating averages with respect to distributions of unobservables, such as moments of individual fixed-effects, or average partial effects in discrete choice models. For such quantities, we propose and study posterior average effects (PAE), where the average is computed conditional on the sample, in the spirit of empirical Bayes and shrinkage methods. While the usefulness of shrinkage for prediction is well-understood, a justification of posterior conditioning to estimate population averages is currently lacking. We show that PAE have minimum worst-case specification error under various forms of misspecification of the parametric distribution of unobservables. In addition, we introduce a measure of informativeness of the posterior conditioning, which quantifies the worst-case specification error of PAE relative to parametric model-based estimators. As illustrations, we report PAE estimates of distributions of neighborhood effects in the US, and of permanent and transitory components in a model of income dynamics.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: cemmap working paper ; No. CWP36/21

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Estimation: General ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
• Thema: model misspecification ; robustness ; sensitivity analysis ; empirical Bayes ; posterior conditioning ; latent variables

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Bonhomme, Stéphane ; Weidner, Martin
• Ereignis: Veröffentlichung
• (wer): Centre for Microdata Methods and Practice (cemmap)
• (wo): London
• (wann): 2021

Weitere Informationen

• DOI: doi:10.47004/wp.cem.2021.3621
• Handle: http://hdl.handle.net/10419/246804
• Letzte Aktualisierung: 20.09.2024, 08:22 MESZ