Results 91 to 100 of about 13,095 (150)

DSGE Estimation Using Generalized Empirical Likelihood and Generalized Minimum Contrast

Entropy
We investigate the performance of estimators of the generalized empirical likelihood and minimum contrast families in the estimation of dynamic stochastic general equilibrium models, with particular attention to the robustness properties under ...
Gilberto Boaretto, Márcio Poletti Laurini   +1 more
doaj   +1 more source

Contemporary challenges in model misspecification

2024In contemporary practical applications, the significance of model misspecification has grown notably in many fields. When statistical inferences are based on likelihoods, model misspecification can give rise to inaccurate uncertainty quantification ...
Li, Jiawei
core   +1 more source

A Cautionary Tale of Model Misspecification and Identifiability

Bulletin of Mathematical Biology
Mathematical models are routinely applied to interpret biological data, with common goals that include both prediction and parameter estimation. A challenge in mathematical biology, in particular, is that models are often complex and non-identifiable, while data are limited.
Alexander P. Browning, Jennifer A. Flegg, Ryan J. Murphy   +2 more
openaire   +3 more sources

Using recurrent neural network to estimate irreducible stochasticity in human choice behavior

eLife
Theoretical computational models are widely used to describe latent cognitive processes. However, these models do not equally explain data across participants, with some individuals showing a bigger predictive gap than others.
Yoav Ger, Moni Shahar, Nitzan Shahar
doaj   +1 more source

Spatial Econometrics Revisited: A Case Study of Land Values in Roanoke County [PDF]

Omitting spatial characteristics such as proximity to amenities from hedonic land value models may lead to spatial autocorrelation and biased and inefficient estimators.
McGuirk, Anya M., Bosch, Darrell J., Kaltsas, Ioannis K.   +2 more
core  

Semi-parametric local variable selection under misspecification [PDF]

Local variable selection aims to test for the effect of covariates on an outcome within specific regions. We outline a challenge that arises in the presence of non-linear effects and model misspecification.
Saez, Ignacio, Guindani, Michele, Kseung, Arnold Kisuk, Rossell, David   +3 more
core   +1 more source

Sequential design augmentation with model misspecification

, 2007
In Response Surface Methodology (RSM) one attempts to model some variable of interest, usually as a known function of design variables. Subsequent analysis often indicates a need to move to a new region of interest.
Sutherland, Sindee S.
core  

Misspecified Bayesian Learning by Strategic Players : First-Order Misspecification and Higher-Order Misspecification

We consider strategic players who may have a misspecified view about the world, and investigate their long-run behavior when they learn an unknown state from public signals over time. Our framework is flexible and allows for higher-order misspecification,
Murooka, Takeshi, Yamamoto, Yuichi
core   +1 more source

Minimizing Sensitivity to Model Misspecification

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
Stéphane Bonhomme, Martin Weidner
core  

Model Misspecification [PDF]

, 2015
Moosa, I., Burns, Kelly
openaire   +2 more sources