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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 — 2026-10-14。
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Policy Gains or Household Need? The Allocation Logic of China's Dibao Program

政策收益还是家庭需要:中国低保项目的分配逻辑

When social assistance is scarce, should it prioritize households in greatest need or those expected to benefit most? Using panel data from the China Household Finance Survey, we distinguish allocation principles by combining predicted policy gains with entry into China's Minimum Living Standard Guarantee (Dibao). We estimate heterogeneous predicted gains in consumption and education and then recover the conditional priorities revealed by recipient selection. Predicted gains…

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A Projection-Based ARIMA Framework for Nonlinear Dynamics in Macroeconomic and Financial Time Series: Closed-Form Estimation and Rolling-Window Inference

非线性宏观与金融时间序列的投影式 ARIMA:闭式估计与滚动推断

We introduce Galerkin-ARIMA and Galerkin-SARIMA, a projection-based extension of classical ARIMA/SARIMA that replaces rigid linear lag operators with low-dimensional Galerkin basis expansions while preserving the familiar AR-MA decomposition. Experiments on synthetic series and on quarterly GDP and daily S&P 500 returns show that Galerkin-SARIMA matches or improves forecast accuracy relative to classical ARIMA/SARIMA. Estimation is closed-form via a two-stage least-squares…

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Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints

结合预测与 VaR 约束的依赖网络投资组合设计

This study proposes a novel portfolio optimization framework that integrates statistical social network analysis with time series forecasting and risk management. Using daily stock data from the S&P 500 (2020-2024), we construct dependency networks via Vector Autoregression (VAR) and Forecast Error Variance Decomposition (FEVD), transforming influence relationships into a cost-based network. Specifically, FEVD breaks down the VAR's forecast error variance to quantify how…

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Matched Design for Marginal Causal Effect on Restricted Mean Survival Time in Observational Studies

观察性研究中限制平均生存时间边际因果效应的匹配设计

Investigating the causal relationship between exposure and the time-to-event outcome is an important topic in biomedical research. Previous literature has discussed the potential issues of using the hazard ratio as a marginal causal effect measure due to its noncollapsibility property. In this paper, we advocate using the restricted mean survival time (RMST) difference as the marginal causal effect measure, which is collapsible and has a simple interpretation as the…

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