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来源与收录范围

汇总期刊目录中的 16 个期刊来源,以及 arXiv 计量经济学与相关统计、金融分类和主题检索中的预印本。期刊题录主要来自 Crossref,可靠摘要保留补充来源;期刊官网链接可在论文检索的说明中核对。

保留计量方法研究、相关统计基础及实质涉及计量方法的应用、教程、比较和综述。待核验、范围外及撤回记录不进入公开简报。基础文献按学习与研究需要单独选编。

此前日简报及周、月简报按可核实的原站发表、首次提交和修订日期归档;历史补录只留在文献库。期刊仅纳入收录当月新发表或提前取得题录的论文;只有月份的记录仅进入对应月简报。arXiv 同期只计一次,首次发布优先;未保存的中间版本无法补算。撤回内容会同步移除;所有历史补录仍可在论文检索中查找。

当前结果:已发表 0 篇 · 待刊 0 篇 · 预印本 3 篇

关键词匹配标题、摘要、作者或 DOI;支持部分字母,多词同时匹配,双引号匹配短语。

本期动态按期刊、arXiv 首次发布、arXiv 修订分组,组内默认本文引用优先。也可选择作者引用优先或最新收录。

期刊论文在前,预印本在后。组内先看本文引用,再看已有明确关联作者的最高累计引用,最后按当前日期顺序排序。

“作者引用”指已有明确关联作者的最高累计引用,不合计共同作者。目前作者指标只来自有身份关联快照的预印本,普通期刊条目可能没有该项数据;本页 0/3 篇有可核验作者指标。

本文引用采用 OpenAlex 单条关联记录,不合计版本;缺失不记作零。累计引用会随时间积累,可结合近年范围比较;引用数和作者发表经历是阅读线索,不代表论文质量或同行评审状态。

“作者发表记录”展示作者已有论文的可核验发表记录,不证明该预印本已接收或通过同行评审。

正常题录、可靠摘要和引用同步继续;AI 解读依据可靠材料,材料不足时保留待解读状态。

本期 3 篇
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arXiv · 本期首次发布

Journal Impact Factor and Federal Reserve Monetary Policy: An Econometric Analysis Based on 1975-2026

期刊影响因子与美联储货币政策:基于1975—2026年的计量分析

The Journal Impact Factor (IF), as a core indicator of academic evaluation, has not been systematically studied in relation to its historical evolution and global macroeconomic environment. This paper employs a period-based regression analysis using long-term time series data from 1975-2026 to examine the statistical relationship between IF and Federal Reserve monetary policy (using real interest rate as a proxy variable). The study estimates three nested models using…

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From Hawkes Processes to Attention: Time-Modulated Mechanisms for Event Sequences

从 Hawkes 过程到注意力:事件序列的时间调制机制

Marked Temporal Point Processes (MTPPs) arise naturally in medical, social, commercial, and financial domains. However, existing Transformer-based methods mostly inject temporal information only via positional encodings, relying on shared or parametric decay structures, which limits their ability to capture heterogeneous and type-specific temporal effects. Inspired by this observation, we derive a novel attention operator called Hawkes Attention from the multivariate Hawkes…

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White noise testing for functional time series via functional quantile autocorrelation

基于函数型分位数自相关的函数型时间序列白噪声检验

We introduce a novel class of nonlinear tests for serial dependence in functional time series, grounded in the functional quantile autocorrelation framework. Unlike traditional approaches based on the classical autocovariance kernel, the functional quantile autocorrelation framework leverages quantile-based excursion sets to robustly capture temporal dependence within infinite-dimensional functional data, accommodating potential outliers and complex nonlinear dependencies.…

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