Alternative TitleTime-varying forecast averaging for air passengers in China
张健; 孙玉莹; 张新雨; 汪寿阳
Source Publication系统工程理论与实践
Abstract机场扩建、政策导向、经济发展等外在因素的变化常常导致航空客运量数据发生结构性改变,其模型的设定也在很大程度上存在不确定性,因此,精准而稳定地预测航空客运量变得十分困难.为了解决以上问题,本文采用了一种时变模型平均方法(TVJMA)(Sun等,2020,2012)对全国Top 5机场的客运量进行了预测研究,该方法在模型平均时基于最小化局部Jackknife准则给出了最优的权重选择,并通过非参数估计,实现了最优权重随时间变化.实证结果表明,本文所采用的TVJMA方法显著优于其它基准模型,包括Hansen和Racine (2012)的Jackknife模型平均(JMA)以及自回归模型(AR),单整自回归移动平均模型(ARIMA),季节性单整自回归移动平均模型(SARIMA)和时变参数模型(TVP)等传统方法.进一步,对不同的预测步长,TVJMA在航空客运量的预测效果同样具有稳健性.因此,TVJMA方法可以有效地降低由于航空客运量的结构性变化和预测模型不确定性等导致的预测风险,进而做出精准而稳定的客运量预测.
Other AbstractStructural changes often occur in air passengers due to some external factors such as airport expansion,policy orientation and economic development;model uncertainty is a common long-standing issue in forecasting.To address these issues,a novel time-varying Jackknife model averaging method(TVJMA)(Sun et al,2020,2012)is employed to predict air passengers of the Top 5 airports in China.Based on nonparametric estimation,the optimal time-varying weights for various candidate models with time-varying parameters in candidate models are obtained by minimizing the local Jackknife criterion at every time point t.TVJMA method allows the weights and parameters to change over time.Empirical results show that the TVJMA method used in this paper is significantly superior to other benchmark models,including Hansen and Racine's(2012)Jackknife model averaging method(JMA),autoregression model(AR),autoregression integrated moving average model(ARIMA),seasonal autoregression integrated moving average model(SARIMA),and time-varying parameter model(TVP).Furthermore,the predictive effect of TVJMA is robust to different test sets and prediction steps.Overall,TVJMA method effectively reduces the predictive risk caused by structural changes and model uncertainty,and thus produces accurate and stable forecasts of air passengers.
Keywordair passengers time-varying model average non-parametric estimation time-varying weights time-varying parameter predictive models 航空客运量 时变模型平均 非参数估计 时变权重 时变参数预测模型
Indexed ByCSCD
Citation statistics
Document Type期刊论文
Recommended Citation
GB/T 7714
张健,孙玉莹,张新雨,等. 基于时变模型平均方法的我国航空客运量预测[J]. 系统工程理论与实践,2020,40(6):1509-1519.
APA 张健,孙玉莹,张新雨,&汪寿阳.(2020).基于时变模型平均方法的我国航空客运量预测.系统工程理论与实践,40(6),1509-1519.
MLA 张健,et al."基于时变模型平均方法的我国航空客运量预测".系统工程理论与实践 40.6(2020):1509-1519.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[张健]'s Articles
[孙玉莹]'s Articles
[张新雨]'s Articles
Baidu academic
Similar articles in Baidu academic
[张健]'s Articles
[孙玉莹]'s Articles
[张新雨]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[张健]'s Articles
[孙玉莹]'s Articles
[张新雨]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.