An Instrumental Variable Approach to Dynamic Models

Generalised instrumental variable models

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 In...

Generalized instrumental variable models

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 In...

Mendelian randomization as an instrumental variable approach to causal inference.

In epidemiological research, the causal effect of a modifiable phenotype or exposure on a disease is often of public health interest. Randomized controlled trials to investigate this effect are not always possible and inferences based on observational data can be confounded. However, if we know of a gene closely linked to the phenotype without direct effect on the disease, it can often be reaso...

An Instrumental Variable Approach to Full-information Estimators for Linear and Non-linear Econometric Models

Errors-in-Variables Identification in Dynamic Networks by an Instrumental Variable Approach ?

In this paper, the objective is to obtain an estimate of a particular module embedded in a dynamic network using noisy measurements of the internal variables. This is an extension of the errors-in-variables (EIV) framework to the case of dynamic networks. The consequence of measuring the variables with noise is that the prediction error identification methods no longer result in consistent esti...