Generalized instrumental variable models

Arbeitspapier

The ability to allow for flexible forms of unobserved heterogeneity is an essential ingredient in modern microeconometrics. In this paper we extend the application of instrumental variable (IV) methods to a wide class of problems in which multiple values of unobservable variables can be associated with particular combinations of observed endogenous and exogenous variables. In our Generalized Instrumental Variable (GIV) models, in contrast to traditional IV models, the mapping from unobserved heterogeneity to endogenous variables need not admit a unique inverse. The class of GIV models allows unobservables to be multivariate and to enter non-separably into the determination of endogenous variables, thereby removing strong practical limitations on the role of unobserved heterogeneity. Important examples include models with discrete or mixed continuous/discrete outcomes and continuous unobservables, and models with excess heterogeneity where many combinations of different values of multiple unobserved variables, such as random coefficients, can deliver the same realizations of outcomes. We use tools from random set theory to study identification in such models and provide a sharp characterization of the identified set of structures admitted. We demonstrate the application of our analysis to a continuous outcome model with an interval-censored endogenous explanatory variable.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: cemmap working paper ; No. CWP04/14

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Econometric and Statistical Methods and Methodology: General ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models ; Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation
• Thema: instrumental variables ; endogeneity ; excess heterogeneity ; limited information ; set identification ; partial identification ; random sets ; incomplete models

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Chesher, Andrew ; Rosen, Adam
• Ereignis: Veröffentlichung
• (wer): Centre for Microdata Methods and Practice (cemmap)
• (wo): London
• (wann): 2014

Weitere Informationen

• DOI: doi:10.1920/wp.cem.2014.0414
• Handle: http://hdl.handle.net/10419/97370
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ