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Identification and Structural Inference of Dynamic Causal Effects: Theory and Applications (ISIDCE)
Start date: Jan 1, 2012, End date: Dec 31, 2015 PROJECT  FINISHED 

"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."

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