We consider conditional moment models under semi-strong identification. Identification strength is directly defined through the conditional moments that flatten as the sample size increases. Our new minimum distance estimator is consistent, asymptotically normal, robust to semi-strong...

This lecture explores conditions under which there is identification of the impact on an outcome of exogenous variation in a variable which is endogenous when data are gathered. The starting point is the Cowles Commission linear simultaneous equations model. The parametric and additive error...

We show that even under incomplete markets, the equilibrium manifold identifies individual demands everywhere in their domains. For this, we assume conditions of smoothness, interiority and regularity, but avoid implausible observational requirements. It is crucial that there be date-zero...

In simultaneous equation (SE) contexts, nuisance parameter, weak instruments and identification problems severely complicate exact and asymptotic tests (except for very specific hypotheses). In this paper, we propose exact likelihood based tests for possibly nonlinear hypotheses on the...

This paper presents Bayesian methodology for the estimation of a bivariate probit model with an endogenous effect and both parametric linear and flexible semiparametric exogenous effects. The model is prompted by an analysis of the utilisation of health services in Australia using data from the...

We show that even under incomplete markets, the equilibrium manifold identifies individual demands everywhere in their domains. For this, we assume conditions of smoothness, interiority and regularity, but avoid implausible observational requirements. It is crucial that there be date-zero...

This paper presents a generalized two-step maximum likelihood estimation method for partially identified vector autoregressive models. We suggest a likelihood ratio test for over-identification in a sub-system and derive the asymptotics for impulse responses and forecast-error variance...