Econometric Theory

Econometric Theory 由 Cambridge University Press 出版，属中科院经济学大类（2025 升级版4区）。按 OpenAlex 近年发文统计，该刊主要发表 Statistical Methods and Inference、Monetary Policy and Economic Impact、Financial Risk and Volatility Modeling 等主题的论文，这 3 个主题约占 45.9%，主导子领域为 Statistics and Probability、Economics and Econometrics。（根据 OpenAlex 主题统计与中科院分区数据生成）

Econometric Theory publishes original, rigorous research that advances the theoretical foundations of econometrics across all major areas of the field. The journal’s mission is to publish research of the highest scholarly quality that strengthens the probabilistic and statistical underpinnings of econometric methodology and provides a solid foundation for modeling, estimation, prediction, and inference. It recognizes the central role of theoretical econometrics in economics, business, and other social sciences, and increasingly in multidisciplinary settings involving statistics, data science, machine learning, and selected natural sciences. The journal welcomes contributions addressing modern challenges arising from large-scale data, high-performance computing, and artificial intelligence, and encourages connections with related disciplines such as probability theory, statistics, mathematical finance, and computational economics. It values work that unifies, extends, or reinterprets econometric theory through new perspectives, generalizations, or more elegant methods, and encourages submissions that contain practical applications of new theory and promote best practices in econometric analysis. In addition to original research articles, the journal also publishes historical studies on the evolution of econometric thought and interviews with leading scholars.Founding Editor and ET Editor 1985-2025Peter C. B. Phillips, Sterling Professor Emeritus of Economics, Cowles Foundation for Research in Economics, Yale University, USA; Distinguished Professor, University of Auckland, New Zealand

摘自期刊官网 Aims & Scope

研究方向（LetPub）：MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY

主编 Patrik Guggenberger出版商官网

中科院期刊分区表

数据库收录

SCIESSCIScopusLetPub

国际期刊预警名单：各版本均未列入LetPub

不同版本、大类与小类分别展示，不将不同评价口径合并为一个分区值；投稿与考核前请确认所在机构采用的版本。

影响力指标

影响因子

0.48LetPub · 2026-09-13

影响因子（出版商公布）

0.6出版商官网 · 2026-09-29

CiteScore 2026-06

2.3LetPub · 2026-09-13

SJR 2026-06

1.62LetPub · 2026-09-13

SNIP 2026-06

1.05LetPub · 2026-09-13

h-index

61LetPub · 2026-09-13

h-index

140OpenAlex · 2026-10-01

i10-index

1,187OpenAlex · 2026-10-01

两年平均被引

1.32OpenAlex · 2026-10-01

自引率

16.7%LetPub · 2026-09-13

Gold OA 文章占比

45.83%LetPub · 2026-09-13

发文规模

年文章数

38LetPub · 2026-09-13

不同来源的统计口径与观测时间不同，页面保留各来源原值；“出版商公布”为期刊官网自述数据。

平均审稿速度（社区反馈）

>12周，或约稿LetPub · 2026-09-13

平均录用比例（社区反馈）

容易LetPub · 2026-09-13

评审方式

单盲同行评审出版商官网

社区反馈来自投稿者样本，置信度 0.5，不代表期刊承诺；“出版商公布”的时间节点定义以官网为准。

混合期刊的文章处理费仅在作者选择开放获取发表时收取；按订阅方式发表通常不收费。

费用以期刊官网为准；不同来源的观测时间可能不同，页面保留各来源原值，不做平均。

摘录来自期刊官网页面，仅保留原文片段；“模型摘要”由 Workers AI 生成并经原文校验，以官网为准。

出版商

Cambridge University Press

出版国家或地区

UNITED STATES

出版语言

English

ISSN

0266-4666

电子版 ISSN

1469-4360

官方链接

• 期刊主页 ↗OpenAlex
• 投稿系统 ↗LetPub