Identifiability criteria for Muth-rational expectations models

The authors deal with complete static linear models that contain current Muth-rational expectations. Rank, order and variety conditions for the identifiability of the structural parameter are derived under general restrictions. We also correct statements that appeared in the literature. Our main finding is that, in general, the standard rank and order conditions are sufficient also for the identifiability of the Muth-rational expectations model parameter, whenever there are enough not fully anticipated exogenous variables. If the number of imperfectly forecasted exogenous variables falls short of the number of endogenous variables by g, then g extra restrictions are needed on every equation and the restrictions must meet easily verifiable variety conditions as well as an augmented rank criterion.     

Local identifiability of the factor analysis and measurement error model parameter

Measurement error regression models are factor analysis models, the latent ‘correct’ regressors are the factors. There is however no common statistical method between the factor analysis and the regression model, because the covariance elements that are known identifying constituents of the former model are unknown in the latter. Instead, the idea that the data come from the same regression model, as with panel data, but can be grouped in two or more groups, each group having its own different regrressor generating process, is shown to supply credible restrictions. We generalize and compare relevant identifiability criteria and corresponding asymptotically efficient estimators that are recursive in the number of overidentifying restrictions.