Info-metric Methods for the Estimation of Models with Group-Specific Moment Conditions

Martyn Andrews, Alastair Hall*, Rabeya Khatoon, James Lincoln

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

Abstract

Motivated by empirical analyses in economics using repeated cross-section data, we propose info-metric methods (IM) for estimation of the parameters of statistical models based on the information in population moment conditions that hold at group level. The info-metric estimation can be viewed as the primary approach to a constrained optimization. The estimators can also be obtained via the dual approach to this optimization, known as Generalized Empirical Likelihood (GEL). In a companion paper, we provide a comprehensive framework for inference based on GEL with the grouped-specific moment conditions. In this chapter, we compare the computational requirements of the primary and dual approaches. We also describe the IM/GEL inference framework in the context of a linear regression model that is estimated using the information that the mean of the error is zero for each group. For the latter setting, we use analytical arguments and a small simulation study to compare the properties of IM/GEL-based inferences to those of inferences based on certain extant methods. The IM/GEL methods are illustrated through an application to estimation of the returns to education in which the groups are defined via information on family background.
Original languageEnglish
Title of host publicationRecent Innovations in Info-Metrics
Subtitle of host publicationa cross-disciplinary perspective on information and information processing
EditorsAmos Golan, Min Chen , Michael Dunn, Aman Ullah
PublisherOxford University Press
Chapter13
ISBN (Print)9780190636685
Publication statusPublished - 20 Jan 2021

Structured keywords

  • ECON Econometrics

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