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Identification and Structural Inference of Dynamic Causal Effects: Theory and Applications

Objective

Identification of causal effects from non-experimental data is a difficult task because of the scarcity of plausible exogenous variation in the data. Typically, variation in the ‘treatment’ variable, such as a policy intervention, is not independent from relevant but unobserved characteristics of the underlying causal relationship. Causal inference often relies on the availability of valid instruments. A serious threat to the internal and external validity of econometric inference arises when the instruments are weak. This problem is well documented and is pervasive across most areas of economics.
The proposed research will make a number of methodological and applied contributions to the current state of the art. First, it will provide improved methods of inference that are robust to the problem of weak instruments. Second, it will propose new approaches to the identification of dynamic causal effects that is particularly relevant for the analysis of macroeconomic policy. Third, it will apply state of the art econometric methods to the study of unemployment and business cycle fluctuations, with particular emphasis on European data.

Field of science

  • /social sciences/economics and business/economics/econometrics

Call for proposal

FP7-PEOPLE-2011-CIG
See other projects for this call

Funding Scheme

MC-CIG - Support for training and career development of researcher (CIG)

Coordinator

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Address
Wellington Square University Offices
OX1 2JD Oxford
United Kingdom
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 100 000
Administrative Contact
Stephen Conway (Dr.)