Results 71 to 80 of about 13,095 (150)
Acknowledgement Misspecification in Macroeconomic Theory [PDF]
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.
Sargent, Thomas-J, Hansen, Lars-Peter
core
Frontiers in Psychology, 2018 The performance of the limited-information statistic M2 for diagnostic classification models (DCMs) is under-investigated in the current literature. Specifically, the investigations of M2 for specific DCMs rather than general modeling frameworks are ...
Fu Chen, Yanlou Liu, Tao Xin, Ying Cui
doaj +1 more source
Efficient Imitation under Misspecification
CoRRWe consider the problem of imitation learning under misspecification: settings where the learner is fundamentally unable to replicate expert behavior everywhere. This is often true in practice due to differences in observation space and action space expressiveness (e.g. perceptual or morphological differences between robots and humans).
Nicolas A. Espinosa Dice +3 more
openaire +3 more sources
Identifying the sources of model misspecification
, 2015 In this paper we propose an empirical method for detecting and identifying misspecification in structural economic models. Our approach formalizes the common practice of adding "shocks" in the model, and identifies potential misspecification via forecast
Kuo, Chun-Hung +2 more
core
Dealing with Misspecification in DSGE Models: A Survey [PDF]
, 2017 Dynamic Stochastic General Equilibrium (DSGE) models are the main tool used in Academia and in Central Banks to evaluate the business cycle for policy and forecasting analyses.
Paccagnini, Alessia
core +2 more sources
CPT: Pharmacometrics & Systems PharmacologyWe address the problem of model misspecification in population pharmacokinetics (PopPK), by modeling residual unexplained variability (RUV) by machine learning (ML) methods in a postprocessing step after conventional model building. The practical purpose
Christos Kaikousidis +2 more
doaj +1 more source
Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC
Frontiers in Ecology and Evolution, 2019 The methods for making statistical inferences in scientific analysis have diversified even within the frequentist branch of statistics, but comparison has been elusive.
Brian Dennis +4 more
doaj +1 more source
Minimum Penalized ϕ-Divergence Estimation under Model Misspecification
Entropy, 2018 This paper focuses on the consequences of assuming a wrong model for multinomial data when using minimum penalized ϕ -divergence, also known as minimum penalized disparity estimators, to estimate the model parameters.
M. Virtudes Alba-Fernández +2 more
doaj +1 more source
Complexity and Misspecification
We propose a tractable model of repeated decision problems that combines concern about model misspecification, as in robust control, with a complexity cost, such as Shannon entropy, that makes pessimistic beliefs trade off statistical plausibility against simplicity. In a static setting, stronger complexity aversion selects more concentrated worst-case
Fudenberg, Drew, Mudekereza, Florian
openaire +2 more sources
Robust Portfolio Optimization with Environmental, Social, and Corporate Governance Preference
RisksThis study addresses the crucial but under-explored topic of ambiguity aversion, i.e., model misspecification, in the area of environmental, social, and corporate governance (ESG) within portfolio decisions.
Marcos Escobar-Anel, Yiyao Jiao
doaj +1 more source