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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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Informational herding with model misspecification [PDF]
Journal of Economic Theory, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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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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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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Model Misspecification in Statistical Analysis [PDF]
, 2017 In my talk, I will discuss two important problems related with model misspecification. How do we provide powerful tests for checking misspecification in hypothesized models?
Zhou, Qian (Michelle)
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Robustness of Regularized Regression Methods Under Compound Model Misspecification: A Simulation Benchmarking Study [PDF]
Tutorials in Quantitative Methods for PsychologyRegularized regressions are widely used in psychological research where the fitted model is assumed to be correctly specified. Yet, psychological data routinely violates assumptions of linearity, homoscedasticity and additivity which raises questions ...
Ramazan, Onur, Lui, Yiu Wa
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Acknowledging Misspecification in Macroeconomic Theory
, 2002 We explore methods for confronting model misspecification in macroeconomics. We construct dynamic equilibria in which private agents and policy makers recognize that models are approximations.
Thomas J. Sargent, Lars Peter Hansen
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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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Multicollinearity and Model Misspecification
Sociological Science, 2016 Multicollinearity in linear regression is typically thought of as a problem of large standard errors due to near-linear dependencies among independent variables. This problem can be solved by more informative data, possibly in the form of a larger sample.
Christopher Winship, Bruce Western
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