Long-term prediction intervals of economic time series

被引:0
|
作者
M. Chudý
S. Karmakar
W. B. Wu
机构
[1] Ministry of Finance,Institute for Financial Policy
[2] University of Vienna,Department of Statistics and Operations Research
[3] University of Florida,Department of Statistics
[4] University of Chicago,Department of Statistics
来源
Empirical Economics | 2020年 / 58卷
关键词
Heavy-tailed noise; Long memory; Kernel quantile estimator; Stationary bootstrap; Bayes; C14; C15; C53; C87; E27;
D O I
暂无
中图分类号
学科分类号
摘要
We construct long-term prediction intervals for time-aggregated future values of univariate economic time series. We propose computational adjustments of the existing methods to improve coverage probability under a small sample constraint. A pseudo-out-of-sample evaluation shows that our methods perform at least as well as selected alternative methods based on model-implied Bayesian approaches and bootstrapping. Our most successful method yields prediction intervals for eight macroeconomic indicators over a horizon spanning several decades.
引用
收藏
页码:191 / 222
页数:31
相关论文
共 50 条
  • [41] Long-Term Prediction of Small Time-Series Data Using Generalized Distillation
    Hayashi, Shogo
    Tanimoto, Akira
    Kashima, Hisashi
    [J]. 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2019,
  • [42] Long-term Prediction of Blood Pressure Time Series Using Multiple Fuzzy Functions
    Abbasi, Robabeh
    Moradi, Mohammad Hassan
    Molaeezadeh, Seyyedeh Fatemeh
    [J]. 2014 21TH IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING (ICBME), 2014, : 124 - 127
  • [43] Evolutionary echo state network for long-term time series prediction: on the edge of chaos
    Zhang, Gege
    Zhang, Chao
    Zhang, WeiDong
    [J]. APPLIED INTELLIGENCE, 2020, 50 (03) : 893 - 904
  • [44] Long-term prediction of small time-series data using generalized distillation
    Hayashi S.
    Tanimoto A.
    Kashima H.
    [J]. Transactions of the Japanese Society for Artificial Intelligence, 2020, 35 (05) : 1 - 9
  • [45] A Toy Model Study for Long-Term Terror Event Time Series Prediction with CNN
    Aishvarya Kumar Jain
    Christian Grumber
    Patrick Gelhausen
    Ivo Häring
    Alexander Stolz
    [J]. European Journal for Security Research, 2020, 5 (2) : 289 - 309
  • [46] Bootstrap prediction intervals for autoregressive time series
    Clements, Michael P.
    Kim, Jae H.
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2007, 51 (07) : 3580 - 3594
  • [47] Long-term time series reversal: International evidence
    Kobinger, Sonja
    Bornholt, Graham
    Malin, Mirela
    [J]. JOURNAL OF INTERNATIONAL FINANCIAL MARKETS INSTITUTIONS & MONEY, 2020, 65
  • [48] A fuzzy model for long-term financial time series
    Watanabe, N
    Kuwabara, M
    [J]. PROCEEDINGS OF THE NINTH IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, 2005, : 76 - 81
  • [49] Long-term prediction of time series based on VMD cyclic reservoir with random jumps network
    Han M.
    Jiang T.
    Feng S.-B.
    [J]. Kongzhi yu Juece/Control and Decision, 2020, 35 (09): : 2175 - 2181