A quantile regression forecasting model for ICT development

被引:4
|
作者
Yu, Tiffany Hui-Kuang [1 ]
机构
[1] Feng Chia Univ, Dept Publ Finance, Taichung 40724, Taiwan
关键词
Information technology; Forecasting; Quantitative techniques; Quantile regression method;
D O I
10.1108/MD-11-2012-0793
中图分类号
F [经济];
学科分类号
02 ;
摘要
Purpose - Because quantile regression gets more popular and provides more comprehensive interpretations, it is important to advance quantile regression for forecasting. By extending the convention quantile regression, the purpose of this paper is to propose a quantile regression-forecasting model to forecast information and communication technology (ICT) development. Design/methodology/approach - This paper proposes an approach to forecasting based on quantile regression method. Findings - Via quantile information criterion, the proposed approach can identify whether the independent variables are predictable. For those which are predictable, the proposed approach can be used to forecast these variables. Practical implications - The proposed approach is used to forecast ICT development. It can also be used to forecast other problem domains. Originality/value - Based on the empirical results, the proposed approach advances the application of quantile regression model to forecast. The applicability of quantile regression model is greatly enhanced.
引用
收藏
页码:1263 / 1272
页数:10
相关论文
共 50 条
  • [31] Tail-Heaviness, Asymmetry, and Profitability Forecasting by Quantile Regression
    Tian, Hui
    Yim, Andrew
    Newton, David P.
    [J]. MANAGEMENT SCIENCE, 2021, 67 (08) : 5209 - 5233
  • [32] Bayesian bootstrap quantile regression for probabilistic photovoltaic power forecasting
    Mokhtar Bozorg
    Antonio Bracale
    Pierluigi Caramia
    Guido Carpinelli
    Mauro Carpita
    Pasquale De Falco
    [J]. Protection and Control of Modern Power Systems, 2020, 5
  • [33] An Improved Quantile Regression Neural Network for Probabilistic Load Forecasting
    Zhang, Wenjie
    Quan, Hao
    Srinivasan, Dipti
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (04) : 4425 - 4434
  • [34] Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms
    Papacharalampous, Georgia
    Langousis, Andreas
    [J]. WATER RESOURCES RESEARCH, 2022, 58 (06)
  • [35] The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective
    Bonaccolto, Giovanni
    Caporin, Massimiliano
    [J]. JOURNAL OF RISK AND FINANCIAL MANAGEMENT, 2016, 9 (03)
  • [36] Quantile Regression Neural Network for Forecasting Inflow and Outflow in Yogyakarta
    Amalia, Farah Fajrina
    Suhartono
    Rahayu, Santi Puteri
    Suhermi, Novri
    [J]. 2ND INTERNATIONAL CONFERENCE ON STATISTICS, MATHEMATICS, TEACHING, AND RESEARCH 2017, 2018, 1028
  • [37] Value at Risk Forecasting Based on Quantile Regression for GARCH Models
    Lee, Sangyeol
    Noh, Jungsik
    [J]. KOREAN JOURNAL OF APPLIED STATISTICS, 2010, 23 (04) : 669 - 681
  • [38] Parametric Quantile Beta Regression Model
    Bourguignon, Marcelo
    Gallardo, Diego I.
    Saulo, Helton
    [J]. INTERNATIONAL STATISTICAL REVIEW, 2024, 92 (01) : 106 - 129
  • [39] Flexible Model Aggregation for Quantile Regression
    Fakoor, Rasool
    Kim, Taesup
    Mueller, Jonas
    Smola, Alexander J.
    Tibshirani, Ryan J.
    [J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2023, 24
  • [40] Dynamic Network Quantile Regression Model
    Xu, Xiu
    Wang, Weining
    Shin, Yongcheol
    Zheng, Chaowen
    [J]. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2024, 42 (02) : 407 - 421