. Multiplicity does not necessarily preclude the estimation of a particular model (and in certain cases even improves its … identification), but ignoring it can lead to misspecifications. The survey starts with a general characterization of structural … models that highlights how multiplicity a.ects the classical paradigm. Because the information structure is an important …

the identification of social interactions. We consider linear and discrete choice models as well as social networks … identification literature, we indicate areas where additional research is especially needed and suggest some directions that appear …

This paper provides a systematic analysis of identification in linear social interactions models. This is both a … of those models. We consider identification of both endogenous (peer) and contextual social effects under alternative … individual-level and aggregated data. Finally, we discuss potential ramifications for identification of endogenous group …

interaction effects. We conduct an experiment that avoids the identification problem present in the field. Our novel design …

interaction effects. We conduct a laboratory experiment that avoids the identification problem present in the field and allows us …

social interaction effects. Testing for such effects raises severe identification problems. We conduct an experiment that …

, with both real and nominal frictions, and with sufficiently wide ranges for their parameterers. This identification …

, based on microeconomic deposit market data. We impose identification restrictions both in the cross-section (across insured …

We show that the main nonparametric identification finding of Abbring and Van den Berg (2003b, Econometrica) for the … identification is based on the competing-risks identification result of Abbring and Van den Berg (2003a, Journal of the Royal …

The notion of the group of orthogonal matrices acting on the set of all feasible identification schemes is used to … characterize the identification problem arising in structural vector autoregressions. This approach presents several conceptual … uninformative prior. Second, it allows to derive the joint distribution of blocks of parameters defining an identification scheme …