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
This lecture explores conditions under which there is identification of the impact on an outcome of exogenous variation in a variable which is endogenous when data are gathered.
This paper provides weak conditions under which there is nonparametric interval identification of local features of a structural function which depends on a discrete endogenous variable and is nonseparable in a latent variate.
This paper provides weak conditions under which there is nonparametric interval identification of local features of a structural function which depends on a discrete endogenous variable and is nonseparable in a latent variate.
Conditions are derived under which there is local nonparametric identification of values of structural functions and of their derivatives in potentially nonlinear nonseparable models.
Weak nonparametric restrictions are developed, sufficient to identify the values of derivatives of structural functions in which latent random variables are nonseparable.
This paper explores the identifiability of ratios of derivatives of the index function in a model of a duration process in which the impact of covariates on the hazard function passes through a single index.
The impact of response measurement error in duration data is investigated using small parameter asymptotic approximations and compared with the effect of hazard function heterogeneity.
Conditions are derived under which there is local nonparametric identification of values of structural functions and of their derivatives in potentially nonlinear nonseparable models.
This paper develops an extension of the classical multinomial logit model which approximates a class of models obtained when there is uncontrolled taste variation across agents and choices in addition to the stochastic noise inherent in the logit model.
Conditions are derived under which there is local nonparametric identification of derivatives of structural equations in nonlinear triangular simultaneous equations systems.
An exogenous impact function is defined as the derivative of a structural function with respect to an endogenous variable, other variables, including unobservable variables held fixed.
This article notes that it is now practical to use the method of enumeration to analyse the performance of estimators and hypothesis tests of fully parametric binary data models.
