An empirical investigation on the forecasting ability of mallows model averaging in a macro economic environment

2012 ◽  
Author(s):  
Yip Chee Yin ◽  
Lim Hock-Eam
Keyword(s):  
Empirical Investigation ◽  
Model Averaging ◽  
Economic Environment ◽  
Mallows Model

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.

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.

scholarly journals AN EMPIRICAL INVESTIGATION ON HOW BETTER HRM PRACTICES HELP EMPLOYEES REDUCE JOB RELATED STRESS AND OVERALL ECONOMIC ENVIRONMENT WRT THE SERVICE SECTOR AND MANUFACTURING SECTION

2021 ◽  
Vol 4 (2) ◽  
pp. 6-17
Keyword(s):  
Corporate Culture ◽  
Service Sector ◽  
Empirical Investigation ◽  
Manufacturing Sector ◽  
Economic Environment ◽  
Hrm Practices ◽  
Related Stress ◽  
Job Related Stress ◽  
Employee Experience ◽  
The Impact

Stress – the Silent Killer, causes a great deal of Damage to the fabric of Organization. Stress can be in the form of Depression or Anxiety, which are the ultimate form of STRESS. The Research Paper Intents to Highlight Job related stress an employee experience at his workplace which can be caused due to various factors in Service as well as Manufacturing Sector and compare which Sector mostly faces the Heat of STRESS and how better HRM practices can help the Employees Cope up with the Stress caused due to Work Assignments. The Purpose of this Research is to study the Impact of Job-related stress on Employees, how do they react to stress and how does it cause a Major Impact on Corporate Culture of an Organization and in turn major losses to Economy of the Country.

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