Comparing the predictive ability of Mallows model averaging and pre-test estimators

2011 ◽  
Author(s):  
Yip Chee Yin ◽  
Lim Hock Eam ◽  
Hong Pei Yee
Keyword(s):  
Model Averaging ◽  
Predictive Ability ◽  
Mallows Model

scholarly journals Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Julia Ledien ◽  
Zulma M. Cucunubá ◽  
Gabriel Parra-Henao ◽  
Eliana Rodríguez-Monguí ◽  
Andrew P. Dobson ◽  
...  
Keyword(s):  
Chagas Disease ◽  
Performance Indicator ◽  
Disease Incidence ◽  
Bootstrap Method ◽  
Model Averaging ◽  
Predictive Ability ◽  
Force Of Infection ◽  
Spatiotemporal Trends ◽  
Urban Rural ◽  
Best Fit

AbstractAge-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.

scholarly journals Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Siqi Xu ◽  
Yifeng Zhang ◽  
Xiaodan Chen
Keyword(s):  
Carbon Dioxide ◽  
Dynamic Model ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Predictive Power ◽  
Carbon Dioxide Emission ◽  
Model Averaging ◽  
Predictive Ability ◽  
Time Varying ◽  
Dynamic Model Averaging

Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods.

OPTIMAL MULTISTEP VAR FORECAST AVERAGING

2020 ◽  
Vol 36 (6) ◽  
pp. 1099-1126
Author(s):  
Jen-Che Liao ◽  
Wen-Jen Tsay
Keyword(s):  
Infinite Order ◽  
Asymptotic Optimality ◽  
Model Misspecification ◽  
Model Averaging ◽  
Finite Sample ◽  
Forecast Horizon ◽  
Simulation Experiments ◽  
Vector Autoregressive ◽  
One Step ◽  
Mallows Model

This article proposes frequentist multiple-equation least-squares averaging approaches for multistep forecasting with vector autoregressive (VAR) models. The proposed VAR forecast averaging methods are based on the multivariate Mallows model averaging (MMMA) and multivariate leave-h-out cross-validation averaging (MCVAh) criteria (with h denoting the forecast horizon), which are valid for iterative and direct multistep forecast averaging, respectively. Under the framework of stationary VAR processes of infinite order, we provide theoretical justifications by establishing asymptotic unbiasedness and asymptotic optimality of the proposed forecast averaging approaches. Specifically, MMMA exhibits asymptotic optimality for one-step-ahead forecast averaging, whereas for direct multistep forecast averaging, the asymptotically optimal combination weights are determined separately for each forecast horizon based on the MCVAh procedure. To present our methodology, we investigate the finite-sample behavior of the proposed averaging procedures under model misspecification via simulation experiments.

An evaluation of prior influence on the predictive ability of Bayesian model averaging

Oecologia ◽  
2011 ◽  
Vol 168 (3) ◽  
pp. 719-726 ◽  
Author(s):  
Véronique St-Louis ◽  
Murray K. Clayton ◽  
Anna M. Pidgeon ◽  
Volker C. Radeloff
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Predictive Ability ◽  
Prior Influence

On the Sparsity of Mallows’ Model Averaging Estimator

2019 ◽  
Author(s):  
Yang Feng ◽  
Qingfeng Liu ◽  
Ryo Okui
Keyword(s):  
Model Averaging ◽  
Mallows Model

scholarly journals On the sparsity of Mallows model averaging estimator

2020 ◽  
Vol 187 ◽  
pp. 108916 ◽  
Author(s):  
Yang Feng ◽  
Qingfeng Liu ◽  
Ryo Okui
Keyword(s):  
Model Averaging ◽  
Mallows Model

Spatial Mallows model averaging for geostatistical models

2019 ◽  
Vol 47 (3) ◽  
pp. 336-351 ◽  
Author(s):  
Jun Liao ◽  
Guohua Zou ◽  
Yan Gao
Keyword(s):  
Model Averaging ◽  
Geostatistical Models ◽  
Mallows Model

scholarly journals INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS

2018 ◽  
Vol 35 (4) ◽  
pp. 816-841 ◽  
Author(s):  
Xinyu Zhang ◽  
Chu-An Liu
Keyword(s):  
Asymptotic Behavior ◽  
Least Squares ◽  
Regression Models ◽  
Model Averaging ◽  
Asymptotic Distributions ◽  
Linear Regression Models ◽  
Nominal Level ◽  
Coverage Probabilities ◽  
Simulation Based ◽  
Mallows Model

This article considers the problem of inference for nested least squares averaging estimators. We study the asymptotic behavior of the Mallows model averaging estimator (MMA; Hansen, 2007) and the jackknife model averaging estimator (JMA; Hansen and Racine, 2012) under the standard asymptotics with fixed parameters setup. We find that both MMA and JMA estimators asymptotically assign zero weight to the under-fitted models, and MMA and JMA weights of just-fitted and over-fitted models are asymptotically random. Building on the asymptotic behavior of model weights, we derive the asymptotic distributions of MMA and JMA estimators and propose a simulation-based confidence interval for the least squares averaging estimator. Monte Carlo simulations show that the coverage probabilities of proposed confidence intervals achieve the nominal level.

scholarly journals On the Tuning Parameter Selection in Model Selection and Model Averaging: A Monte Carlo Study

2019 ◽  
Author(s):  
Hui Xiao ◽  
Yiguo Sun
Keyword(s):  
Monte Carlo ◽  
Model Selection ◽  
Sample Size ◽  
Model Averaging ◽  
Parameter Selection ◽  
Tuning Parameter ◽  
High Dimensional ◽  
Finite Sample ◽  
Sparse Models ◽  
Mallows Model

Model selection and model averaging have been the popular approaches in handling modelling uncertainties. Fan and Li(2006) laid out a unified frame work for variable selection via penalized likelihood. The tuning parameter selection is vital in the optimization problem for the penalized estimators in achieving consistent selection and optimal estimation. Since the OLSpost-LASSO estimator by Belloni and Chernozhukov (2013), few studies have focused on the finite sample performances of the class of OLS post-penalty estimators with the tuning parameter choice determined by different tuning parameter selection approaches. We aim to supplement the existing model selection literature by studying such a class of OLS post-selection estimators. Inspired by the Shrinkage Averaging Estimator (SAE) by Schomaker(2012) and the Mallows Model Averaging (MMA) criterion by Hansen (2007), we further propose a Shrinkage Mallows Model Averaging (SMMA) estimator for averaging high dimensional sparse models. Based on the Monte Carlo design by Wang et al. (2009) which features an expanding sparse parameter space as the sample size increases, our Monte Carlo design further considers the effect of the effective sample size and the degree of model sparsity on the finite sample performances of model selection and model averaging estimators. From our data examples, we find that the OLS post-SCAD(BIC) estimator in finite sample outperforms most of the current penalized least squares estimators as long as the number of parameters does not exceed the sample size. In addition, the SMMA performs better given sparser models. This supports the use of the SMMA estimator when averaging high dimensional sparse models.

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