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Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data
Econometrics, 2019 Researchers are often faced with the challenge of developing statistical models with incomplete data. Exacerbating this situation is the possibility that either the researcher’s complete-data model or the model of the missing-data mechanism is ...
Richard M. Golden +3 more
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Likelihood-based estimation and prediction for a measles outbreak in Samoa
Infectious Disease Modelling, 2023 Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response. Differential equations are often used to model an epidemic outbreak's behaviour but are challenging to parameterise. Furthermore, these
David Wu +4 more
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Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification
Econometrics, 2015 Allowing for misspecification in the linear conditional quantile function, this paper provides a new interpretation and the semiparametric efficiency bound for the quantile regression parameter β (
Ying-Ying Lee
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Structured ambiguity and model misspecification
Journal of Economic Theory, 2022 A decision maker is averse to not knowing a prior over a set of restricted structured models (ambiguity) and suspects that each structured model is misspecified. The decision maker evaluates intertemporal plans under all of the structured models and, to recognize possible misspecifications, under unstructured alternatives that are statistically close ...
Lars Peter Hansen, Thomas J. Sargent
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An Information Criterion for Auxiliary Variable Selection in Incomplete Data Analysis
Entropy, 2019 Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables.
Shinpei Imori, Hidetoshi Shimodaira
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Making Decisions under Model Misspecification [PDF]
SSRN Electronic Journal, 2020 Abstract We use decision theory to confront uncertainty that is sufficiently broad to incorporate “models as approximations.” We presume the existence of a featured collection of what we call “structured models” that have explicit substantive motivations.
Cerreia–Vioglio, Simone +3 more
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EconometricaEmpirical research typically involves a robustness‐efficiency tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax some of these assumptions to motivate a more robust, but variable, unrestricted estimator.
Armstrong, Timothy B. +2 more
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Misspecification in Inverse Reinforcement Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function R from a policy pi. To do this, we need a model of how pi relates to R. In the current literature, the most common models are optimality, Boltzmann rationality, and causal entropy maximisation. One of the primary motivations behind IRL is to infer human preferences from human
Joar Skalse, Alessandro Abate
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Confronting model misspecification in macroeconomics [PDF]
Journal of Econometrics, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniel F. Waggoner, Tao Zha
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Target Matrix Estimators in Risk-Based Portfolios
Risks, 2018 Portfolio weights solely based on risk avoid estimation errors from the sample mean, but they are still affected from the misspecification in the sample covariance matrix.
Marco Neffelli
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