Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold

Zhang, Xinyu; Wan, Alan T. K.; Zhou, Sherry

doi:10.1198/jbes.2011.10075

Cited by 78 publications

(49 citation statements)

References 40 publications

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“…Reference [86] does it for the quantile regression model. Reference [82] extends it for the dependent time series models or models with GARCH errors. Also, using MMA method in [39], for models with endogeneity, in [87] develops MMA based two-stage least squares (MATSLS), model averaging limited information maximum likelihood (MALIML), and model averaging Fuller (MAF) estimators.…”

Section: Jackknife Model Averaging Methods (Cv)mentioning

confidence: 99%

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Parametric and Nonparametric Frequentist Model Selection and Model Averaging

Ullah¹,

Wang²

2013

Econometrics

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This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated.

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Section: Jackknife Model Averaging Methods (Cv)mentioning

confidence: 99%

“…While the Bayesian model averaging estimator (BMAE) has a neat interpretation, it searches for the true model instead of selecting an estimator of a model with a low loss function. In simulations it has been found that SAIC and SBIC tend to outperform AIC and BIC estimators, see [82].…”

Section: Bayesian and Fic Weightsmentioning

confidence: 99%

Parametric and Nonparametric Frequentist Model Selection and Model Averaging

Ullah¹,

Wang²

2013

Econometrics

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“…Combining these models seems to be more reasonable than choosing one of them. Averaging weights can be based on the scores of information criteria (Buckland, Burnham and Augustin (1997), Hjort and Claeskens (2003), Claeskens, Croux and van Kerckhoven (2006), Zhang and Liang (2011), Zhang, Wan, and Zhou (2012)). Other model averaging strategies that have been developed include, for example, the adaptive regression by mixing of Yang (2001), the Mallows model averaging (MMA) of Hansen (2007) (see also Wan, Zhang, and Zou (2010)), and the optimal mean squared error averaging of Liang et al (2011).…”

Section: Introductionmentioning

confidence: 99%

Model averaging based on Kullback-Leibler distance

Zhang¹,

Zou²,

Carroll³

2015

STAT SINICA

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This paper proposes a model averaging method based on Kullback-Leibler distance under a homoscedastic normal error term. The resulting model average estimator is proved to be asymptotically optimal. When combining least squares estimators, the model average estimator is shown to have the same large sample properties as the Mallows model average (MMA) estimator developed by Hansen (2007). We show via simulations that, in terms of mean squared prediction error and mean squared parameter estimation error, the proposed model average estimator is more efficient than the MMA estimator and the estimator based on model selection using the corrected Akaike information criterion in small sample situations. A modified version of the new model average estimator is further suggested for the case of heteroscedastic random errors. The method is applied to a data set from the Hong Kong real estate market.

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“…See, for example, Yuan and Yang () and Zhang et al. () for a discussion of when model averaging performs better than model selection.…”

Section: Introductionmentioning

confidence: 99%

Heteroscedasticity‐robustCpmodel averaging

Liu

Okui

2013

The Econometrics Journal

122

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Summary This paper proposes a new model‐averaging method, called the hetero‐scedasticity–robust Cp (HRCp) method, for linear regression models with heteroscedastic errors. We provide a feasible form of the Mallows’ Cp‐like criterion for choosing the weight vector for averaging. Under some regularity conditions, we show that the HRCp method has asymptotic optimality. The simulation results show that our method works well and performs better than alternative methods in finite samples when the number of candidate models is large and/or the population coefficient of determination is not small.

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Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold

Cited by 78 publications

References 40 publications

Parametric and Nonparametric Frequentist Model Selection and Model Averaging

Parametric and Nonparametric Frequentist Model Selection and Model Averaging

Model averaging based on Kullback-Leibler distance

Heteroscedasticity‐robustCpmodel averaging

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