Academic Journal
Q1Journal of Econometrics
About Journal of Econometrics
Journal of Econometrics is a scholarly journal published by Elsevier B.V.. SCImago 2025 places it in Q1 with an SJR of 6.575 and an H-index of 205.
Its listed coverage is 1973-2026 and its research categories include Applied Mathematics (Q1); Economics and Econometrics (Q1). The 2025 dataset reports 185 documents and 3219 citations across the latest three-year reporting window.
The Journal of Econometrics is a highly regarded academic journal dedicated to the advancement of econometric research. Published by Elsevier, this journal serves as a critical platform for scholars, researchers, and policymakers to explore cutting-edge methodologies, theories, and applications in econometrics. Since its inception, the Journal of Econometrics has played a vital role in shaping the field by providing high-quality, peer-reviewed research that influences economic policy and decision-making worldwide.
Scope and Impact of the Journal
The Journal of Econometrics covers a broad range of topics within econometrics, including but not limited to:- Time series analysis
- Panel data econometrics
- Bayesian econometrics
- Machine learning applications in econometrics
- Structural modeling
- Causal inference and experimental methods
- Forecasting techniques
- Nonparametric and semiparametric methods
Why the Journal of Econometrics is Highly Regarded
1. High Impact Factor and Citation Influence
The journal is widely recognized for its scholarly impact, boasting a high citation rate and influence on econometric literature. Researchers frequently reference articles published in the Journal of Econometrics, further solidifying its reputation as a leading source of econometric knowledge.2. Prestigious Editorial Board
The journal's editorial board consists of renowned econometricians from top institutions worldwide. Their expertise ensures that only the highest-quality research is accepted, maintaining the journal’s credibility and rigor.3. Peer-Reviewed and Quality-Assured Content
Each submission undergoes a rigorous peer-review process, ensuring that only well-researched, innovative, and methodologically sound papers are published. This process upholds the integrity and academic excellence of the journal.4. Integration of Advanced Methodologies
With the rise of big data and machine learning, the journal has expanded its focus to include modern econometric techniques. These advancements allow economists to develop more accurate predictive models and data-driven insights, making the journal a crucial resource for contemporary research.Submission and Readership
The Journal of Econometrics welcomes submissions from academics, data scientists, and policymakers who wish to contribute to the field of econometrics. The journal publishes monthly, ensuring a continuous flow of fresh research and insights. Its readership includes university professors, graduate students, financial analysts, and government agencies seeking empirical and theoretical contributions to econometric analysis.Journal Metrics
Metrics can change by reporting year. Verify time-sensitive values with the publisher or indexing service.
Aims & Scope
The Journal of Econometrics is a leading academic publication dedicated to advancing the field of econometrics. It serves as a crucial platform for researchers, economists, and statisticians to present innovative methodologies, empirical studies, and theoretical advancements in econometric analysis. This article explores the scope of the Journal of Econometrics, highlighting its key focus areas, significance, and contributions to the field of economic research.
Key Focus Areas of the Journal
1. Theoretical Econometrics
The journal publishes groundbreaking research in the development of econometric models and theories. Topics include probability theory, statistical inference, and mathematical modeling techniques that enhance econometric applications.2. Applied Econometrics
Applied econometrics is a major focus, covering empirical studies that apply econometric methods to real-world economic issues. Research includes areas such as market analysis, policy evaluation, and economic forecasting using statistical and computational techniques.3. Time Series and Panel Data Analysis
The journal emphasizes advanced methodologies for analyzing time series and panel data. These techniques are crucial for understanding economic trends, financial market behavior, and macroeconomic policy effects.4. Machine Learning and Big Data in Econometrics
With technological advancements, the Journal of Econometrics has expanded its scope to include machine learning applications and big data analytics in economic research. This integration helps improve model accuracy and predictive capabilities.5. Bayesian and Nonparametric Econometrics
Bayesian inference and nonparametric methods play a vital role in modern econometrics. The journal explores these techniques to refine estimation processes and improve decision-making in economic models.6. Financial Econometrics
Financial markets are increasingly analyzed through econometric techniques. The journal features research on asset pricing, risk management, volatility modeling, and high-frequency trading using econometric methodologies.Significance of the Journal in Economic Research
The Journal of Econometrics is instrumental in shaping economic policies and financial decision-making worldwide. By providing rigorous peer-reviewed research, it ensures that policymakers, analysts, and academicians have access to reliable econometric insights. Additionally, the journal fosters interdisciplinary collaboration between economics, statistics, and data science.Contributions to the Academic and Professional Community
- Innovative Research: Encourages the development of new econometric techniques to address contemporary economic challenges.
- Policy Impact: Assists governments and organizations in making data-driven economic policies.
- Education and Training: Serves as an essential resource for students, educators, and professionals aiming to enhance their econometric skills.
- Global Reach: The journal's contributions extend across various economic sectors, including finance, healthcare, and labor markets.
Recent Research Articles
Latest publications matched automatically by ISSN.
Clustering with potential multidimensionality: Inference and practice
Ruonan Xu, Luther Yap
2026-11 · DOI: 10.1016/j.jeconom.2026.106321Matrix-valued spatial autoregressions with dynamic heterogeneous spillovers
Yicong Lin, Andre Lucas, Shiqi Ye
2026-11 · DOI: 10.1016/j.jeconom.2026.106323Identification of non-additive fixed effects models: Is the return to teacher quality homogeneous?
Jinyong Hahn, John D. Singleton, Neşe Yıldız
2026-11 · DOI: 10.1016/j.jeconom.2026.106332Spatio-temporal autoregressions for high dimensional matrix-valued time series
Baojun Dou, Jing He, Sudhir Tiwari, Qiwei Yao et al.
2026-11 · DOI: 10.1016/j.jeconom.2026.106319Iterative distributed multinomial regression
Yanqin Fan, Yigit Okar, Xuetao Shi
2026-11 · DOI: 10.1016/j.jeconom.2026.106334Testing for structural changes in panel data models with interactive fixed effects via discrete Fourier transform
Yifei Fang, Zhonghao Fu, Song Han, Xia Wang et al.
2026-11 · DOI: 10.1016/j.jeconom.2026.106333Multivariate inference for dynamic systemic risk measures
Yuan Chen, Nikolaus Hautsch, Jérémy Leymarie, Melanie Schienle et al.
2026-11 · DOI: 10.1016/j.jeconom.2026.106322Normal approximation for U-statistics with cross-sectional dependence
Weiguang Liu
2026-11 · DOI: 10.1016/j.jeconom.2026.106325Principal component analysis for a mix of stationary and nonstationary variables
James D. Hamilton, Xinwei Ma, Jin Xi
2026-11 · DOI: 10.1016/j.jeconom.2026.106317Robust inference for time varying predictability: A Sieve-IVX approach
Nan Liu, Yanbo Liu, Peter C.B. Phillips, Yajie Zhang et al.
2026-11 · DOI: 10.1016/j.jeconom.2026.106320MCA: High-dimensional modal component analysis towards the mode
Zhe Sun, Yundong Tu
2026-09 · DOI: 10.1016/j.jeconom.2026.106274Estimation and inference on average treatment effect in percentage points under heterogeneity
Ying Zeng
2026-09 · DOI: 10.1016/j.jeconom.2026.106303Asymptotics of CoVaR inference in two-quantile-regression
Xuan Leng, Yi He, Yanxi Hou, Liang Peng et al.
2026-09 · DOI: 10.1016/j.jeconom.2026.106301Instrumental variable regression with varying-intensity repeated treatments
Jaerim Choi, Dakyung Seong, Shu Shen
2026-09 · DOI: 10.1016/j.jeconom.2026.106305A goodness-of-fit test for sparse networks
Yujia Wu, Wei Lan, Long Feng, Chih-Ling Tsai et al.
2026-09 · DOI: 10.1016/j.jeconom.2026.106276Testing coefficient stability in spatial regression
Ulrich K. Müller, Mark W. Watson
2026-09 · DOI: 10.1016/j.jeconom.2026.106309Testing for underpowered literatures
Stefan Faridani
2026-09 · DOI: 10.1016/j.jeconom.2026.106312Identification-robust inference for the LATE with high-dimensional covariates
Yukun Ma
2026-09 · DOI: 10.1016/j.jeconom.2026.106302Graph-based multisample comparison with application to feature selection for multi-category responses
Dan Pu, Haoming Shi, Wei Lan, Chih-Ling Tsai et al.
2026-09 · DOI: 10.1016/j.jeconom.2026.106313Model averaging for time–varying vector autoregressions
Yuying Sun, Feng Chen, Jiti Gao
2026-09 · DOI: 10.1016/j.jeconom.2026.106308Reviews
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April 16, 2025 at 11:03 am
April 3, 2025