The Review of Mathematical Economics vol.1 (2026), no.2 has been published
31st August, 2026
The international academic journal The Review of Mathematical Economics (https://intlpress.com/rme) has officially published its second issue.The journal is honored to have Academician Shing-Tung Yau serve as Honorary Editor-in-Chief, and Professor Fei Long as Editor-in-Chief.
This issue features five papers, each reflecting recent advances at the frontier of interdisciplinary research in economics. Please review the following content and scan the QR code for further details
Scholars are cordially invited to engage with the journal and to contribute your work.
1. Title: Geometric Pooling for Combining Density Forecasts
Authors: Yuyuan Wang, Jie Hu, and Yuhong Yang
DOI: 10.4310/RME.260730232358
Abstract: Density forecasting, particularly for time series data, provides a comprehensive characterization of future uncertainty by estimating the entire predictive distribution, whereas point forecasting summarizes the future by a single value. It plays an important role in many applications, including financial risk management, where predictive distributions are used to evaluate quantities such as Value-at-Risk (VaR). Although many methods are available for density forecasting, their empirical performance can differ substantially across applications, and identifying the best method in a given setting is often difficult. Forecast combination provides a practical and effective alternative, and in some cases may outperform all individual candidate forecasts. In this paper, we propose a relaxed-constraint geometric pooling method and its penalized version for combining density forecasts, and establish theoretical properties for the resulting optimized weights. The numerical results suggest that the proposed methods work well in settings where the simplex constraint is restrictive, while also illustrating that performance gains depend on the forecasting environment and the choice of the penalty rate.
2. Title: Using Machine Learning for Prediction and Policy Analysis in Economics
Authors: Jiajing Sun,Michael Cole, and Wolfgang Karl Härdle
DOI:10.4310/RME.260730231155
Abstract: Machine learning (ML) is now part of the normal empirical toolkit of economics, but its contribution is uneven across tasks and must be judged through economic criteria. In forecasting and measurement, flexible algorithms help economists use high-dimensional administrative data, text, images, digital traces, and labor-market sequences to construct variables that were previously unobserved or observed only with delay. In causal work, ML is most useful when it estimates nuisance functions inside orthogonal, doubly robust, or sample-split procedures that protect low-dimensional treatment-effect parameters from first-stage over-fitting. In structural, financial, and market-design applications, ML expands the feasible computational frontier, but identification, equilibrium reasoning, and counterfactual discipline are still derived from economic structure. In operational settings, such as credit scoring, inspections, humanitarian targeting, central-bank communication, and social protection, predictive accuracy is only one input in a broader welfare, fairness, transparency, and governance problem. This review, therefore, reads the recent literature as a set of task-specific research designs, rather than as a generic contest between algorithms and econometrics. We pay particular attention to what representative papers actually do—their data, target, identifying or validation strategy, and substantive contribution—and to the conditions under which ML improves economic evidence, rather than merely adding complexity.
3. Title: Public Emergencies and Advanced Technology Adoption: Theory and Firm-Level Evidence
Authors: Shihan Li,Qingfu Liu,Yiuman Tse,Christina Dan Wang, and Xiao Wei
DOI:10.4310/RME.260731234637
Abstract: Public emergencies reduce social welfare but may paradoxically stimulate corporate innovation through crisis-driven technological adoption. This study develops a theoretical framework that yields testable implications: exogenous shocks strengthen technology adoption incentives, especially for firms with lower initial technology endowments, while adoption responses vary with firm size. Empirically, we construct a firm-level digital transformation index through textual analysis of a multi-source media database in China, demonstrating that digital transformation enhances firm resilience by boosting capital market performance during public emergencies--particularly for medium-sized enterprises, given the costs and necessity of digital upgrading. Overall, this research contributes to the evidence that public emergencies can accelerate advanced technology adoption.
4. Title: Estimating a VECM for a Small Open Economy
Authors: Sebastian Ankargren and Johan Lyhagen
DOI:10.4310/RME.260731000003
Abstract: In economic theory, the term small open economy refers to an economy that is too small to influence the surrounding world. The surrounding world can, for this reason, be seen as exogenous relative to the economy of this small open economy. The main contribution of this paper is the proposal of how to estimate a vector error correction model with exogeneity restrictions on the long-run and short-run adjustment parameters as well as on the short-run dynamic parameters between small open economies and the surrounding world. A Monte Carlo simulation study of impulse responses shows that the proposed method is considerably more efficient compared to models that fully or partially ignore the restrictions implied by the small open economy hypothesis. Using two Swedish macroeconomic datasets, we find that there are, for some variables, large differences in impulse responses between our proposed method incorporating the restrictions and models using no or partial restrictions. As the small open economy hypothesis is in many situations uncontroversial, our method enables the incorporation of indisputable economic theory into the econometric estimation of the model.
5. Title: Data Empowerment and Bank Performance
Authors: Zhen Li,Ke Song, Shiying Wen and Jiawen Yang
DOI:10.4310/RME.260731230144
Abstract: This study examines how data empowerment affects bank performance. We use large language models to construct a domain-specific dictionary of bank data empowerment and apply it to bank annual reports and patent filings, yielding indicators that capture both the strategic orientation of data empowerment and the innovation activities that implement it. We find that data empowerment significantly enhances bank performance, and we identify three channels that account for part of this effect: growth in demand deposits, expansion of personal loan portfolios, and greater revenue diversification. The effect is stronger for stateowned banks, for banks with more concentrated ownership, and for banks facing higher economic policy uncertainty. These findings establish data as a core strategic resource in modern banking and point to the value of integrating internal and external data sources.
The Review of Mathematical Economics is an interdisciplinary journal that bridges mathematical applications and economic research. The journal is dedicated to publishing high-level papers spanning theoretical and empirical analyses for economists, practitioners, and policymakers. The journal covers all the fields of economics.
The journal upholds the highest standards of academic independence and integrity. Each submission is evaluated according to objective, fair, and professional criteria. It is committed to providing an open, rigorous, and innovative international platform for scholarly exchange where ideas are developed and perspectives are refined.
