跳到正文
收录说明
收录范围
保留计量方法研究、相关统计基础,以及实质涉及计量方法的应用、教程、比较和综述。书评、刊务及明确无关内容不公开;身份或相关性证据不足的记录保留待核,补充可靠材料后可重新判断。
预印本来自 arXiv 计量经济学及相关统计、金融分类和主题检索。主题检索从 2021 年起回补;关注作者追溯更早年份,部分历史文献按已核验编号补录。收录不表示通过同行评审,仍可能遗漏。
本库首次提交范围:2004-06-23 — 2026-10-13。
阅读范围
默认显示本库全部预印本。可选“优先阅读”:至少一位作者同时满足有可核验的本站覆盖期刊正式发表记录,且 OpenAlex 总引用超过 1,000 次。按同一作者身份匹配,两项缺一不可。此筛选提供阅读线索,不作为论文质量判断。
日期与排序
默认按最近修订排序;年份和日期筛选使用首次提交时间。同一 arXiv 编号的修订会合并。仅有月份或年份时,日期区间相交也会纳入。

日期精度:到日 3 篇 · 到月 0 篇 · 到年 0 篇

分类与引用
领域和方法依据题名、摘要初步分类,可人工修正;分类不代表论文质量。引用数采用 OpenAlex 单条关联记录,不合计版本。
更新与 AI 解读
正常题录、可靠摘要与引用同步继续;日、周、月简报按核实的发表与修订日期汇总,历史补录保留在文献库。AI 解读依据可靠摘要或已核实原文片段,已有解读保留供阅读参考;缺少可靠材料的条目保持待解读,浏览页面不会触发生成。
3 篇论文
导出

Difference-in-Differences in the Presence of Unknown Interference

未知干扰存在时的双重差分

The stable unit treatment value (SUTVA) is a crucial assumption in the Difference-in-Differences (DiD) research design. It rules out hidden versions of treatment and any sort of interference and spillover effects across units. Even if this is a strong assumption, it has not received much attention from DiD practitioners and, in many cases, it is not even explicitly stated as an assumption, especially the no-interference assumption. In this technical note, we investigate what…

AI 解读阅读原文 ↗BibTeX ↓

Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis

考试成绩能否改善教师的学生分轨建议?主分层分析

Every year, over one million EU students choose a secondary school track based on teacher recommendations, yet little evidence shows this yields optimal assignments. Using Dutch data, we examine whether access to standardized test scores improves recommendation quality. We develop a Principal-Stratification metric in a quasi-randomized setting, conduct a welfare analysis that flexibly weights short- and long-term losses, and assess principal fairness by examining whether…

AI 解读阅读原文 ↗BibTeX ↓

Estimating the Intensive Margin Effect in Panel Data Settings

面板数据中集约边际效应的估计

Many policies operate through two different channels: the extensive margin (e.g., the decision to participate) and the intensive margin (e.g., the intensity of the response among participants). This paper develops a novel identification strategy to estimate the intensive margin effect in panel data settings. I adapt the Horowitz-Manski-Lee bounds to the Changes-in-Changes framework to partially identify both the average and quantile intensive margin treatment effects.…

AI 解读阅读原文 ↗BibTeX ↓