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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。

期刊按可核实的首次发表日期归档;仅纳入收录当月新发表或已提前取得题录的论文,历史补录只留在文献库。只有月份的记录仅进入对应月简报,只有年份或日期待核的不进简报。arXiv 按原站 UTC 首次提交日和当前版本修订日分组,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 4 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 0/4 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 4 篇
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arXiv · 本期首次发布

Three's a crowd: Identification challenges in the triple difference model with spillover effects

三者成群:存在溢出效应时三重差分模型的识别挑战

The paper studies identification in triple-difference designs when spillover effects contaminate one or more control groups. We show that, under conventional identifying assumptions, the triple-difference model fails to identify both the treatment effect and the spillover effect under such interference. To overcome this limitation, we propose an alternative specification, the double-triple-difference model, and explicitly formalize identifying assumptions and spillover…

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Fully Functional Weighted Testing for Abrupt and Gradual Location Changes in Functional Time Series

函数型时间序列突发与渐进位置变化的全函数加权检验

Change point tests for abrupt changes in the mean of functional data, i.e., random elements in infinite-dimensional Hilbert spaces, are either based on dimension reduction techniques, e.g., based on principal components, or directly based on a functional CUSUM (cumulative sum) statistic. The former have often been criticized as not being fully functional and losing too much information. On the other hand, unlike the latter, they take the covariance structure of the data into…

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A Nonlinear Target-Factor Model with Attention Mechanism for Mixed-Frequency Data

面向混频数据的注意力非线性目标因子模型

We propose the Mixed-Panels-Transformer Encoder (MPTE), a framework for estimating factor models in panels with mixed frequencies and nonlinear signals. Classical factor models rely on linear signal extraction and homogeneous sampling frequencies, limiting their use when variables arrive at different frequencies. MPTE instead uses Transformer-style attention to construct context-aware signals, replacing fixed linear combinations with adaptive reweighting. We extend principal…

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arXiv · 本期修订

What Impulse Response Do Instrumental Variables Identify?

工具变量究竟识别哪一种脉冲响应?

The local projection-instrumental variable (LP-IV) literature has been largely silent on cases in which impulse responses are set-identified, arising when the shock of interest is composite and instruments are correlated with multiple components. We demonstrate that LP-IV estimands constructed using one instrument at a time identify affine combinations of impulse responses to structural shock components with instrument-specific and potentially negative weights, challenging…

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