Structural equation models constrain mean vectors and covariance matrices are frequently applied in the social sciences. Frequently, structural model is misspecified to some extent. In many cases, researchers nevertheless intend work with a target of interest. this article, simultaneous statistical inference for sampling errors misspecification discussed. A modified formula variance matrix para...

We consider estimation under model misspecification where there is a mismatch between the underlying system, which generates data, and used during estimation. propose framework enables joint treatment of types having fake features as well incorrect covariance assumptions on unknowns noise. present decomposition output error into components that relate to different subsets parameters correspondi...

In this paper, we show the first order validity of the block bootstrap in the context of Kolmogorov type conditional distribution tests when there is dynamic misspecification and parameter estimation error. Our approach differs from the literature to date because we construct a bootstrap statistic that allows for dynamic misspecification under both hypotheses. We consider two test statistics; o...

We explore model misspecification in an observational learning framework. Individuals learn from private and public signals and the actions of others. An agent’s type specifies her model of the world; misspecified types have incorrect beliefs about the signal distribution, how other agents draw inference and/or others’ payoffs. We establish that the correctly specified model is robust in that a...

“Robust standard errors” are used in a vast array of scholarship to correct standard errors for model misspecification. However, when misspecification is bad enough to make classical and robust standard errors diverge, assuming that it is nevertheless not so bad as to bias everything else requires considerable optimism. And even if the optimism is warranted, settling for a misspecified model, w...

Likelihood-based methods of inference of population parameters from genetic data in structured populations have been implemented but still little tested in large networks of populations. In this work, a previous software implementation of inference in linear habitats is extended to two-dimensional habitats, and the coverage properties of confidence intervals are analyzed in both cases. Both sta...

A decision maker fears that data are generated by a statistical perturbation of an approximating model that is either a controlled diffusion or a controlled measure over continuous functions of time. A perturbation is constrained in terms of its relative entropy. Several different two-player zero-sum games that yield robust decision rules and are related to one another, to the max-min expected ...

We propose a new finite sample corrected variance estimator for the linear generalized method of moments (GMM) including one-step, two-step, and iterated estimators. Our formula also corrects over-identification bias in estimation on top commonly used correction Windmeijer (2005), which from estimating efficient weight matrix, so is doubly corrected. An important feature proposed double that it...