Results 31 to 40 of about 13,398 (256)
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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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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Decision-Making Under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets
EntropyDistributionally Robust Optimisation (DRO) protects risk-averse decision-makers by considering the worst-case risk within an ambiguity set of distributions based on the empirical distribution or a model. To further guard against finite, noisy data, model-
Charita Dellaporta +2 more
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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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Experiment 2: Misspecification and censoring.
, 2023 Relative bias of the MHRATT estimation methods for varying censoring rates and different DGPs. Sample size is n = 1500, true MHRATT = 0.8 and good overlap (k = 1). In Scenario (A) there is no model misspecification. (TIF)
Guilherme W. F. Barros (17598966) +2 more
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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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Behavioral Coping With Food Price Inflation: Patterns Among U.S. Households
Applied Economic Perspectives and Policy, EarlyView.ABSTRACT Recent food price inflation has placed substantial pressure on household budgets, food security, and mental well‐being. Using Household Pulse Survey data, we combine k‐modes clustering and Double Machine Learning to identify distinct household coping groups and examine differences in how households respond to rising food prices across groups ...
Yuxiang Zhang, Yizao Liu
wiley +1 more source
Modeling Model Misspecification in Structural Equation Models
Stats, 2023 Structural equation models constrain mean vectors and covariance matrices and are frequently applied in the social sciences. Frequently, the structural equation model is misspecified to some extent.
Alexander Robitzsch
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Exploring the Impact of Meat Alternative Labeling Regulations on the U.S. Meat Consumption Patterns
Agribusiness, EarlyView.ABSTRACT The global demand for conventional meat continues to rise, but it is also associated with substantial environmental and health challenges. In response, meat alternatives have gained popularity, sparking debates over meat alternative labeling regulations. This study investigates the effects of meat alternative labeling regulations in the United
Jeong Hun Ji, Sang Hyeon Lee
wiley +1 more source
Specifying Turning Point in Piecewise Growth Curve Models: Challenges and Solutions
Frontiers in Applied Mathematics and Statistics, 2017 Piecewise growth curve model (PGCM) is often used when the underlying growth process is not linear and is hypothesized to consist of phasic developments connected by turning points (or knots or change points).
Ling Ning, Wen Luo
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