This paper studies inference in models that are identified by moment restrictions. We show how instability of the moments can be used constructively to improve the identification of structural parameters that are stable over time. A leading example is macroeconomic models that are immune to the...

Microeconomic data often have within-cluster dependence. This dependence affects standard error estimation and inference in regression models, including the instrumental variables model. Standard corrections assume that the number of clusters is large, but when this is not the case, Wald and...

We present methods for testing hypotheses and estimating confidence sets for structural parameters of economic models in the presence of instabilities and breaks of unknown form. These methods constructively explore information generated by changes in the data-generating process to improve the...

Limited-information identification-robust methods on the indexation and price rigidity parameters of the New Keynesian Phillips Curve yield very wide confidence intervals. Full-information methods impose more restrictions on the reduced-form dynamics and thus make more efficient use of the...

We present a minimum distance approach for conducting hypothesis testing in the presence of potentially weak instruments. Under this approach, we propose size-correct tests for limited dependent variable models with endogenous explanatory variables such as endogenous tobit and probit models....

We propose tests for structural parameters in limited dependent variable models with endogenous explanatory variables. These tests are based upon the generalized minimum distance principle. They are of the correct size regardless of whether the structural parameters are identified and are...