A representative agent fears that his model, a continuous time Markov process with jump and diffusion components, is misspecified and therefore uses robust control theory to make decisions. Under the decision maker’s approximating model, that cautious behavior puts adjustments for model misspecification into market prices for risk factors. We use a statistical theory of detection to quantify ho...

This paper proposes a misspecification-robust iid bootstrap for the generalized method of moment estimators and establishes asymptotic refinements of the symmetric percentile-t bootstrap confidence interval. The paper extends results of Hall and Horowitz (1996) and Andrews (2002). In particular, the proposed method does not involve recentering the moment function in implementing the bootstrap, ...

Typically, model misspecification is addressed by statistics relying on model-residuals, i.e., on one-step ahead forecasting errors. In practice, however, users are often interested in problems involving (also) multi-step ahead forecasting performances, which are not explicitly addressed by traditional diagnostics. In this article, we consider the topic of misspecification from the perspective ...

1.1. Foundations. von Neumann and Morgenstern (1944), Savage (1954), and Muth (1961) created mathematical foundations that applied economists have used to construct quantitative dynamic models for policy making. These foundations give modern dynamic models an internal coherence that lead to sharp empirical predictions. When we acknowledge that models are approximations, logical problems emerge ...

In this paper, Slepian-Bangs-type formulas for Complex Elliptically Symmetric distributed (CES) data vectors in the presence of model misspecification are provided. The basic Slepian-Bangs (SB) formula has been introduced in the array processing literature as a convenient and compact representation of the Fisher Information Matrix (FIM) for parameter estimation under (parametric) Gaussian data ...

In recent years, analyses of neighbourhood externalities have grown with the perceived importance of their influence upon outcomes. Despite this growth, a clear understanding of the role of neighbourhoods in determining outcomes remains elusive. Various attempts have been made to quantify the role of neighbourhoods and limit problems of misspecification that have plagued this literature. Recent...

We consider inference in models defined by approximate moment conditions. show that near?optimal confidence intervals (CIs) can be formed taking a generalized method of moments (GMM) estimator, and adding subtracting the standard error times critical value takes into account potential bias from misspecification In order to optimize performance under misspecification, weighting matrix for this G...

This paper proposes a Sieve Simulated Method of Moments (Sieve‐SMM) estimator for the parameters and distribution shocks in nonlinear dynamic models where likelihood moments are not tractable. An important concern with SMM, which matches sample simulated moments, is that parametric required. However, economic quantities depend on this distribution, such as welfare asset prices, can be sensitive...

The purpose of this study was to review the distinction between formative- and reflective-indicator measurement models, articulate a set of criteria for deciding whether measures are formative or reflective, illustrate some commonly researched constructs that have formative indicators, empirically test the effects of measurement model misspecification using a Monte Carlo simulation, and recomme...

We investigate the use of Markov models of evolution for reduced amino acid alphabets or bins of amino acids. The use of reduced amino acid alphabets can ameliorate effects of model misspecification and saturation. We present algorithms for 2 different ways of automating the construction of bins: minimizing criteria based on properties of rate matrices and minimizing criteria based on propertie...