Andrew is the Director of the ESRC Centre for Microdata Methods and Practice (cemmap) and William Stanley Jevons Professor of Economics and Economic Measurement at University College London. He is a Fellow of the British Academy, Fellow of the Econometric Society and Honorary Foreign Member of the American Economic Association. His research interests cover many aspects of microeconometric theory and practice with currently an emphasis on identification analysis and instrumental variable models and methods.
Education
DocSc Degree (Honoris Causa) , University of Birmingham, 2017
BSoSc (First Class) Mathematics, Economics & Statistics, University of Birmingham, 1970
In this paper the authors 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.
The ability to allow for flexible forms of unobserved heterogeneity is an essential ingredient in modern microeconometrics. In this paper the authors extend the application of instrumental variable (IV) models 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.
This paper was presented at the 2013 Cowles Foundation for Research in Economics conference in Econometrics: Partial Identification, Weak Identification, and Related Econometric Problems. The conference took place on 5 June 2013 at YALE SOM, Watson Center.
This paper was presented at Mathematisches Forschungsinstitut Oberwolfach conference on "Mathematical Statistics of Partially Identified Objects" on 22 April 2013.
This paper studies identification in multiple discrete choice models in which there may be endogenous explanatory variables, that is, explanatory variables that are not restricted to be distributed independently of the unobserved determinants of latent utilities.
We study models with discrete endogenous variables and compare the use of two stage least squares (2SLS) in a linear probability model with bounds analysis using a nonparametric instrumental variable model.
I study the partial identifying power of structural single-equation threshold-crossing models for binary responses when explanatory variables may be endogenous.
We study models with discrete endogenous variables and compare the use of two stage least squares (2SLS) in a linear probability model with bounds analysis using a nonparametric instrumental variable model.
This paper studies identification of latent utility functions in multiple discrete choice models in which there may be endogenous explanatory variables, that is explanatory variables that are not restricted to be distributed independently of the unobserved determinants of latent utilities.
