This paper proposes a forecasting method based on averaged quantile regression to improve predictions for non-normally distributed data. Traditional forecasting models often rely on ordinary least squares, assuming normally distributed errors, which can be restrictive in practice. By leveraging quantile regression, our approach provides a more robust alternative that captures the varying effects of predictors across different quantiles of the response variable. The key contribution of this study is to introduce averaged quantile regression (AQR) as a flexible and effective forecasting tool for high-dimensional, non-normally distributed time series. We show that AQR outperforms conventional mean-based forecasting in a factor modeling setting and remains robust across diverse heavy-tailed, skewed, and near-normal distributions. While our method can be applied broadly, we illustrate its effectiveness within the dynamic factor model framework through numerical experiments and real data analysis.
Impact Factor: 0.1
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Journal of Time Series Econometrics
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Editor-in-Chief:
Javier Hidalgo
Language:
English
First published:
January 1, 2009
Publication Frequency:
2 issues per year
About this journal
Objective
The principal aim of the Journal of Time Series Econometrics (JTSE) is to serve as an internationally recognized outlet for important new research in both theoretical and applied classical and Bayesian time series, spatial, and panel data econometrics. The scope of the journal includes papers dealing with estimation, testing, and other methodological aspects involved in the application of time series and spatial analytic techniques to economic, financial, and related data.
Thanks to its electronic format, the Journal of Time Series Econometrics can publish authors’ algorithms, programs, and data sets alongside traditional articles, allowing other scholars to replicate empirical results, and encouraging applied researchers and practitioners to reproduce and extend academic findings. Authors are invited to assist replication of empirical results by providing copies of data and programs online.
Topics- theoretical and applied classical and Bayesian time series
- spatial econometrics
- panel data econometrics
Article formats
Research articles
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Your benefits:
- A pioneer journal in the field
- Important cutting-edge research in both theoretical and applied time series, spatial, and panel data econometrics
- Wide range of methodological aspects that are involved when applying time series and spatial analytic techniques to economic and financial data
- Interdisciplinary debate with participation of economists and statisticians
History
Content available since 2009 (Volume 1, Issue 1)
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Requires Authentication UnlicensedForecasting High-Dimensional Non-Normal Time Series Using Averaged Quantile RegressionLicensedAugust 19, 2025
| Journal Impact Factor | 0.1 | 2024, Journal Citation Reports (Clarivate, 2025) |
| 5-year Journal Impact Factor | 0.7 | 2024, Journal Citation Reports (Clarivate, 2025) |
| Journal Citation Indicator | 0.23 | 2024, Journal Citation Reports (Clarivate, 2025) |
| CiteScore | 1.7 | 2024, Scopus (Elsevier B.V., 2025) |
| SCImago Journal Rank | 0.168 | 2024, SJR (Scimago Lab, 2025; Data Source: Scopus) |
| Source Normalized Impact per Paper | 0.340 | 2024, CWTS Journal Indicators (CWTS B.V., 2025; Data Source: Scopus) |
| Mathematical Citation Quotient | 0.03 |
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Editor-in-Chief
Javier Hidalgo, London School of Economics
Advisory Editors
Pierre Perron, Boston University
Associate Editors
Yoosoon Chang, Texas A&M University
Xiaohong Chen, Yale University
Tim Conley, University of Chicago
Niels Haldrup, Aarhus University
Yongmiao Hong, Cornell University
Lajos Horvath, University of Utah
Lutz Killian, University of Michigan
Gary Koop, University of Strathclyde
Stephen Leybourne, Nottingham University
Efstathios Paparoditis, University of Cyprus
Joon Park, Texas A&M University
Joris Pinkse, Pennsylvania State University
Carlos Velasco, Universidad Carlos III
Tim Vogelsang, Michigan State University
Zhijie Xiao, Boston University
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Journal information
Additional information
eISSN:
1941-1928
Language:
English
Publisher:
De Gruyter
Additional information
First published:
January 1, 2009
Publication Frequency:
2 issues per year
eISSN:
1941-1928
Audience(s) for this journal
macroeconomists, applied statisticians, and financial analysts
