Abstract not found

Abstract We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of √ n-consistent estimators whose cardinality increases with sample size. A special case of our framework corresponds to the conditional moment restriction and the implied estimator in that case is shown to achieve the semiparametric efficiency bound. The proofs do not rely on smoothness of underlying criterion functions. Journal of Economic Literature Classification: C12, C13, C14

Abstract We consider models defined by a set of moment restrictions that may be subject to weak identification. Following the recent literature, the identification of the structural parameters is characterized by the Jacobian of the moment conditions. We unify several definitions of identification that have been used in the literature, and show how they are linked to the consistency and asymptotic normality of GMM estimators. We then develop two tests to assess the identification strength of the structural parameters in models that are (i) either linear or separable; (ii) neither linear nor separable. Both tests are straightforward to apply and allow to test specific subvectors without assuming identification of the components not under test. In simulations, our tests are well-behaved when compared to contenders, both in terms of size and power. In addition, we show how pretesting specific subvectors can improve inference by delivering shorter confidence regions with comparable coverage probability. Finally, when applied to the estimation of the Elasticity of Intertemporal Substitution, our test indicates weak instruments in a larger number of countries than tests proposed by Stock and Yogo (2005) and Montiel

Evaluating the strength of identification in DSGE models. An a priori approach

This paper studies nonlinear, threshold, models in which some of the regressors can be endogenous. An estimation strategy based on instrumental variables was originally developed for dynamic panel models and we extend it to time series models. We apply this methodology to a forward-looking Taylor rule where nonlinearity is introduced via inflation thresholds.

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