Results 11 to 20 of about 23,103 (324)
Minimizing sensitivity to model misspecification [PDF]
Quantitative Economics, 2022 We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments.
Weidner, Martin, Bonhomme, Stéphane
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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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Estimation under Model Misspecification with Fake Features [PDF]
IEEE Transactions on Signal Processing, 2022 We consider estimation under model misspecification where there is a model mismatch between the underlying system, which generates the data, and the model used during estimation.
Ahlén, Anders +2 more
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On the Model-Misspecification in Reinforcement Learning [PDF]
, 2023 The success of reinforcement learning (RL) crucially depends on effective function approximation when dealing with complex ground-truth models. Existing sample-efficient RL algorithms primarily employ three approaches to function approximation: policy-based, value-based, and model-based methods.
Yunfan Li, Lin Yang
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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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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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A View on Model Misspecification in Uncertainty Quantification [PDF]
BNAIC/BENELEARN, 2023 An initial version of the current work has been accepted to be presented at BNAIC/BeNeLearn 2022, to which it was submitted on August 27 ...
Yuko Kato, David M. J. Tax, Marco Loog
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Reconsidering the implications of formative versus reflective measurement model misspecification [PDF]
Information Systems Journal, 2023 The literature on formative modelling (“formative measurement”) in the information systems discipline claims that measurement model misspecification, where a reflective model is used instead of a more appropriate formative model, is widespread.
Marakas, George M. +2 more
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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
doaj +2 more sources
Model misspecification in approximate Bayesian computation : consequences and diagnostics [PDF]
Journal of The Royal Statistical Society Series B-statistical Methodology, 2019 We analyze the behavior of approximate Bayesian computation (ABC) when the model generating the simulated data differs from the actual data generating process; i.e., when the data simulator in ABC is misspecified. We demonstrate both theoretically and in
Rousseau, Judith +9 more
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