It is with great enthusiasm and a profound sense of responsibility that we assume the roles of joint Editors-in-Chief of Econometric Theory (ET), effective from 1 January 2026. Founded in 1985 by Peter C. B. Phillips to champion rigorous and innovative econometric theory at a time of rapidly growing demand, ET has long been a premier outlet for seminal contributions that define the frontiers of our field.
We are committed to advancing this mission while embracing the transformative opportunities of a new era—one defined by high-performance computing, rapid advances in artificial intelligence, and expanding empirical possibilities.
The journal’s core mission, however, remains unwavering: to publish research of the highest quality and rigor that advances the theoretical foundations of econometrics. Since its inception, ET has been the dedicated home for work that deepens the probabilistic and statistical foundations of the discipline, providing the essential bedrock for sound modeling, estimation, prediction, and inference.
Theoretical econometrics plays a central, enabling role not only in economics but also across the business and social sciences. It provides the formal framework that underpins empirical inquiry in fields from finance and marketing to political science and sociology. The principles of identification, estimation, testing, and forecasting developed within our field are the very tools that separate scientific insight from mere pattern recognition. Its deep connections to statistics, modern data science, machine learning, and areas of the natural sciences, such as biostatistics, climate science, and environmental modeling, highlight its increasingly multidisciplinary reach.
Today, the field thrives at the intersection of tradition and innovation. Classical econometrics (cross-sectional, time-series, and panel data econometrics) now confronts environments of new scale and complexity. Advances in machine learning, artificial intelligence, and large-scale computation have broadened empirical possibilities, yet credible inference still requires solid theoretical underpinning. High-dimensional regularization, inference after machine learning, and causal inference with complex observational data represent some of the central challenges of the current landscape. Important areas of ongoing theoretical development include methods for text and image data, network and spatial dependence, ultra-high-frequency observations, and functional data. These developments demand theory that is both deep and practical: mathematically rigorous, computationally feasible, and empirically illuminating.
ET will remain uncompromising in its standards. We seek original contributions that advance the state of the art and are motivated by the complex problems emerging from applied fields. We encourage submissions that push the boundaries of the known and forge new connections between traditional econometric paradigms and the evolving landscape of data science, ensuring that theoretical guarantees keep pace with empirical innovations.
1 NEW INITIATIVES AND FORWARD LOOK
To invigorate discourse and mirror the evolving frontiers of econometrics, we will launch several new initiatives while upholding ET’s tradition of publishing exceptional standalone articles.
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• Invited papers and discussions: We will periodically commission leading scholars to author papers on topics of pressing theoretical importance. Each will be paired with incisive comments from other experts, fostering synthesis, debate, and new research directions. These curated collections will serve as indispensable resources for seasoned researchers and graduate students alike.
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• Theme issues: Dedicated special issues will accelerate progress in cutting-edge areas. Initial themes include:
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○ Econometric Theory for Machine Learning and Artificial Intelligence
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○ Causal Inference under Complex Interference and Heterogeneity
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○ Robust Inference and Partial Identification
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○ Inference in High-Dimensional and Non-Standard Environments
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○ Econometrics of Networks and Spatial Interactions
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These issues are intended to enable breakthroughs and help propel the field forward. Calls for papers will follow in due course.
2 A CALL TO THE COMMUNITY
The strength of ET lies in its community: the authors, referees, editors, and readers whose intellectual engagement defines the journal. We extend a heartfelt invitation to you all to shape this new chapter.
To authors: We invite you to submit your most ambitious and innovative work. Whether your paper resolves a long-standing open problem, proposes a novel method, or forges a new interdisciplinary link, ET is your venue.
We particularly encourage submissions from early-career scholars, whose technical prowess and bold ideas will shape the future of our field. To nurture emerging talent and to honor Peter C. B. Phillips’ extraordinary dedication over more than five decades to mentoring young researchers, we are delighted to establish the biennial Peter C. B. Phillips Award for the best paper published in ET and authored by a scholar within 6 years of receiving their first Ph.D. in any discipline. Further details will be announced in due course.
We are committed to a careful, fair, and efficient review process that provides authors with timely and constructive feedback. Our goal is to reach an initial decision within 4 months of (re)submission.
To our coeditors, associate editors, and referees: Peer review is the lifeblood of academic publishing, and ET has long relied on the specialist expertise and personal dedication of its editors and reviewers. Your careful evaluation, thoughtful advice, and unwavering commitment to meticulous standards have sustained the journal at the highest level, even as the frontiers of econometrics continue to expand and the demands of reviewing grow ever more challenging. We are deeply grateful for the contributions of past editors and reviewers and warmly welcome our new colleagues to this collaborative effort. We invite all editors and reviewers, both longstanding and new, to continue assessing submissions with the depth and care they deserve. To recognize exemplary service, we will establish an annual Best AE and Reviewer Award.
We are excited to embark on this journey. The years ahead promise profound advances in how we learn from data. ET will stand at the forefront, helping shape these developments by providing rigorous foundations. We look forward to working with you to define the future of our discipline.
Patrik Guggenberger, Liangjun Su, and Yixiao Sun
Joint Editors-in-Chief, Econometric Theory
