Jackknife model averaging

By B. Hansen, Jeffrey S. Racine. 2012

512 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/37860369b10811a9b95a3109b06cb31bd7ce4a46

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In Statistics and Probability

Citations in the corpus

Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models (2016, methodology): “2011), jackknife model averaging (Hansen and Racine 2012), heteroskedasticityrobust Cp (Liu and Okui 2013), and optimal model averaging for linear mixed-effects models (Zhang, Zou, and Liang 2014).”
Generalized Least Squares Model Averaging (2016, unknown): “Thus, at least in our simulation design, we deduce that our method can reduce risk more than model selection methods examined in the simulation experiments of Hansen (2007) and Hansen and Racine (2012).”
Variable Selection in Predictive Regressions (2013, methodology): “This is the method of model averaging pioneered by Bates and Granger (1969), reviewed in Timmermann (2006), and further developed in Hansen (2008); Hansen and Racine (2012).”
Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach, Second Version (2013, methodology): “The homoskedasticity restriction can be avoided by instead using leave-one-out cross validation as in Hansen and Racine (2012), which is a generally valid selection criterion under heteroskedasticity.”
Averaging estimators for autoregressions with a near unit root (2010, background): “0 otherwise, where Fc is de�ned in ( 24 ). The AMSE of the Mallows averaging estimator is”
Model averaging based on leave-subject-out cross-validation (2016, methodology): “Thus, we have LsoCV(w) = Y′(A(w) + Q(w))′(A(w) + Q(w))Y. (4) Hansen and Racine (2012) and Zhang et al. (2013) studied the JMA method in linear cross-sectional case.”
A Model-Averaging Approach for High-Dimensional Regression (2014, methodology): “MAIC, model averaging with the Akaike information criterion under the restriction ∑M k=1 wk = 1; MBIC, model averaging with the Bayesian information criterion under the restriction ∑M k=1 wk = 1; MCVHR, model averaging with cross-validation under the restriction ∑M k=1 wk = 1 (Hansen and Racine 2012”
INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS (2018, unknown): “…(Hansen, 2007; Wan, Zhang, and Zou, 2010; Liu and Okui, 2013; Zhang, Zou, and Liang, 2014), optimal mean squared error averaging (Liang, Zou, Wan, and Zhang, 2011), jackknife model averaging (Hansen and Racine, 2012; Zhang, Wan, and Zou, 2013; Lu and Su, 2015), and plug-in averaging (Liu, 2015).”
Distribution Theory of the Least Squares Averaging Estimator (2013, unknown): “Note that both Hansen (2007) and Hansen and Racine (2012) consider an infinite-order regression model and make no distinction between core and auxiliary regressors, which is different from our framework.”
Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods (2019, unknown): “Instead of using simple average, JMA uses leave-one-out cross-validation to estimate optimal weights; see Hansen and Racine (2012) and Zhang, Wan, and Zou (2013).”
Model averaging by jackknife criterion in models with dependent data (2013, background): “All rights reserved. doi:10.1016/j.jeconom.2013.01.004 In a recent article, Hansen and Racine (2012) (hereafter referred to as HR, 2012) developed a jackknife model averaging (JMA) estimator that selects model weights byminimizing a crossvalidation criterion.”
Model averaging and weight choice in linear mixed-effects models (2014, methodology): “…AIC, and BIC; model average estimators based on smoothed AIC and smoothed BIC weights (Buckland et al., 1997); jackknife model averaging, as proposed by Hansen & Racine (2012); and the maximum likelihood estimator under the model including all variables and using the most complex structure of D.”
Model Averaging and Its Use in Economics (2017, methodology): “Hansen and Racine (2012) introduced another estimator within the FMA framework called jackknife model averaging (JMA) that selects appropriate weights for averaging models by minimizing a cross-validation (leave-one-out) criterion.”
Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series (2018, methodology): “…information criterion (AIC) and Bayesian information criterion (BIC) model averaging (Akaike 1979; Raftery, Madigan, and Hoeting 1997; Claeskens and Hjort 2008), the Mallows Cp model averaging (Hansen 2007; Wan, Zhang, and Zou 2010), and the jackknife model averaging (Hansen and Racine 2012).”
A weight-relaxed model averaging approach for high-dimensional generalized linear models (2017, background): “Following Li (1987), Hansen (2007), Wan, Zhang and Zou (2010), Hansen and Racine (2012) and Ando and Li (2014), we demonstrate that our weight selection procedure is asymptotically optimal in certain sense.”