On improvability of model selection by model averaging

Peng, Jingfu; Yang, Yuhong

doi:10.1016/j.jeconom.2020.12.003

Cited by 16 publications

(6 citation statements)

References 32 publications

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“…When the sample size becomes larger, the model selection uncertainty gets lower and model averaging may have no real advantage. This finding is consistent with the results from Yuan and Yang (2005) and Peng and Yang (2021).…”

Section: Discussionsupporting

confidence: 93%

“…Other than these, no asymptotic results for the limiting distribution of the model averaging estimator were presented in this scenario. Peng and Yang (2021) discussed a key question concerning when model averaging can outperform model selection. In the second scenario in which the true model is included in the candidate models, Zhang (2015) showed that MMA and JMA estimators are √ n-consistent.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Asymptotic Theory for Least Squares Model Averaging With Nested Models

2022

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Theoretical results of frequentist model averaging mainly focus on asymptotic optimality and asymptotic distribution of the model averaging estimator. However, even for basic least squares model averaging, many theoretical problems have not been well addressed yet. This article discusses asymptotic properties of a class of least squares model averaging methods with nested candidate models that includes the Mallows model averaging (MMA) of Hansen (2007, Econometrica 75, 1175–1189) as a special case. Two scenarios are considered: (i) all candidate models are under-fitted; and (ii) the true model is included in the candidate models. We find that in the first scenario, the least squares model averaging method asymptotically assigns weight one to the largest candidate model and the resulting model averaging estimator is asymptotically normal. In the second scenario with a slightly special weight space, if the penalty factor in the weight selection criterion is diverging with certain order, the model averaging estimator is asymptotically optimal by putting weight one to the true model. However, MMA with fixed model dimensions is not asymptotically optimal since it puts nonnegligible weights to over-fitted models. The theoretical results are clearly summarized with their restrictions, and some critical implications are discussed. Monte Carlo simulations confirm our theoretical results.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

An Asymptotic Theory for Least Squares Model Averaging With Nested Models

2022

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“…However, the multi-model method [ 27 – 29 ] provides the possibility for stable runoff prediction. Among them, model average method is an effective method to deal with model uncertainty and improve model performance and stability [ 30 , 31 ], mainly including Bayesian Model Average (BMA) [ 27 ] and Frequency Model Average (FMA) [ 28 ]. Among them, BMA is one of the most commonly and widely used methods to generate reliable model prediction.…”

Section: Introductionmentioning

confidence: 99%

Improved runoff forecasting based on time-varying model averaging method and deep learning

et al. 2022

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In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.

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“…For semiparametric models, Judge and Mittelhammer (2007) and DiTraglia (2016) consider averaging GMM estimators, and Kitagawa and Muris (2016) analyze averaging semiparametric estimators of the treatment effects on the treated based on different parametric propensity score models. Averaging estimators in nonparametric models are also discussed, for example, by Fan and Ullah (1999), Yang (2001Yang ( , 2003, Wasserman (2006), and Peng and Yang (2022). Magnus, Powell, and Prüfer (2010) and Fessler and Kasy (2019), among others, investigate Bayesian model averaging estimators as well.…”

Section: Related Literaturementioning

confidence: 99%

An Averaging Estimator for Two-Step M-Estimation in Semiparametric Models

Shi

2022

Econom. Theory

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In a two-step extremum estimation (M-estimation) framework with a finite-dimensional parameter of interest and a potentially infinite-dimensional first-step nuisance parameter, this paper proposes an averaging estimator that combines a semiparametric estimator based on a nonparametric first step and a parametric estimator which imposes parametric restrictions on the first step. The averaging weight is an easy-to-compute sample analog of an infeasible optimal weight that minimizes the asymptotic quadratic risk. Under Stein-type conditions, the asymptotic lower bound of the truncated quadratic risk difference between the averaging estimator and the semiparametric estimator is strictly less than zero for a class of data generating processes that includes both correct specification and varied degrees of misspecification of the parametric restrictions, and the asymptotic upper bound is weakly less than zero. The averaging estimator, along with an easy-to-implement inference method, is demonstrated in an example.

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On improvability of model selection by model averaging

Cited by 16 publications

References 32 publications

An Asymptotic Theory for Least Squares Model Averaging With Nested Models

An Asymptotic Theory for Least Squares Model Averaging With Nested Models

Improved runoff forecasting based on time-varying model averaging method and deep learning

An Averaging Estimator for Two-Step M-Estimation in Semiparametric Models

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