Elections Have Consequences: The Impact of Political Agency on Climate Policy and Asset Prices

Replication package for "Elections Have Consequences: The Impact of Political Agency on Climate Policy and Asset Prices" (William Cassidy), forthcoming in the Journal of Financial Economics. This package contains the code and author-generated data needed to reproduce the tables and figures in the paper. The paper studies how political agency shapes the origination and pricing of climate transition risk, combining a structural model of elections, climate policy, and asset prices with an empirical analysis of high-frequency asset-price reactions to presidential climate-policy announcements. Contents: - Full analysis code (Python, R, Stata, MATLAB, SAS) for the data pipeline, the option-implied expected-return (SVIX) construction, the regressions, and all figures/tables. - A README following the Social Science Data Editors template, documenting software requirements, execution order, and a mapping from each manuscript table/figure to the program that produces it. - Author-derived intermediate data that may be redistributed: the public-domain presidential-remark transcripts and the fitted 260-topic LDA weights used to construct the climate/energy text measures. Proprietary source data are NOT included and cannot be redistributed under their license terms: NYSE TAQ Millisecond, OptionMetrics IvyDB US, and CRSP (all via WRDS subscription), and Gallup polling series. The README gives full acquisition instructions, variable definitions, and the exact datasets used so that a researcher with the corresponding subscriptions can regenerate every intermediate file. The high-frequency market panel is rebuilt from WRDS TAQ; the option-implied expected returns follow Martin (2017) and Martin–Wagner (2019). Software: Python 3.10 (pandas 1.5.3, numpy 1.24.2), R 4.3.x (fixest, data.table), and MATLAB and SAS (for the option-implied SVIX construction). The only stochastic step (the LDA topic model) uses a fixed seed and is deterministic. Approximate end-to-end runtime is 1–3 days. License: This dataset is released under CC BY 4.0. The author-generated code in the package is additionally released under the MIT license (see LICENSE.txt); neither license extends to the proprietary source data (WRDS, Gallup), which are not included.本复现包为论文《选举自有其后果：政治代理对气候政策与资产定价的影响》（作者：William Cassidy）的配套材料，该论文即将发表于《Journal of Financial Economics》（金融经济学期刊）。 本包包含复刻论文中所有表格与图表所需的代码及作者生成的数据集。该论文聚焦于政治代理如何塑造气候转型风险的产生与定价，将选举、气候政策与资产定价的结构化模型，与针对总统气候政策公告的高频资产价格反应实证分析相结合。 内容包括： - 覆盖全流程的分析代码（支持Python、R、Stata、MATLAB、SAS），用于数据流水线构建、期权隐含预期收益（SVIX）的生成、回归分析以及所有图表与表格的绘制。 - 遵循《Social Science Data Editors》（社会科学数据编辑）模板编写的README文档，其中列明了软件需求、执行顺序，以及每一份手稿表格/图表与其生成程序的对应映射关系。 - 作者生成的可再分发中间数据集：可公开获取的总统演讲文本转录稿，以及用于构建气候/能源文本测度的拟合260主题潜在狄利克雷分配（Latent Dirichlet Allocation，LDA）权重。 专有源数据未包含在本包中，且根据其授权条款无法进行再分发，具体包括纽约证券交易所（New York Stock Exchange, NYSE）TAQ毫秒级数据、OptionMetrics IvyDB US以及CRSP数据集（均需通过WRDS订阅获取），以及盖洛普民意调查系列。README文档提供了完整的数据获取指南、变量定义以及所用数据集的详细信息，以便拥有对应订阅权限的研究人员能够重新生成所有中间文件。本研究的高频市场面板数据由WRDS TAQ重构而成；期权隐含预期收益的构建参考了Martin（2017）与Martin–Wagner（2019）的研究方法。 所需软件包括：Python 3.10（配套pandas 1.5.3、numpy 1.24.2）、R 4.3.x（配套fixest、data.table包），以及MATLAB与SAS（用于期权隐含SVIX的构建）。本复现过程中唯一的随机步骤（LDA主题模型）使用了固定随机种子，因此结果具有确定性。整体端到端运行时长约为1至3天。 授权协议：本数据集采用CC BY 4.0协议发布。本包中的作者生成代码额外采用MIT协议发布（详见LICENSE.txt）；上述授权条款均不适用于未包含在内的专有源数据（WRDS、盖洛普数据）。