Inference in High Dimensional Panel Models with an Application to Gun Control
高维面板模型的推断及枪支管制应用AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究个体固定效应面板及内生变量的工具变量推断，并用于估计枪支普及率对犯罪的影响。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
高维面板模型的推断及枪支管制应用
- 研究问题
- 当时变解释变量多于样本量、个体固定效应可与解释变量相关时，如何利用稀疏近似实现面板回归及多工具变量模型的有效推断？
- 方法与思路
- 作者消除个体固定效应后进行变量选择，采用允许组内任意依赖、组间独立的 Lasso 变体，并为固定维度参数构造推断程序。
- 材料陈述的贡献
- 摘要称其在线性固定效应及面板工具变量模型中提供统一有效推断，给出变量选择条件，并以模拟和枪支普及率对犯罪的应用展示方法。
- 阅读关注点
- 应核对消除固定效应后的稀疏近似条件、分组依赖与推断步骤，尤其是犯罪应用中的工具变量有效性及目标效应解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审未核实。摘要未给出具体识别假设、效应数值和区间，不能据此认定枪支普及率的因果影响。
原文摘要
We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high dimensional setting. The setting allows the number of time varying regressors to be larger than the sample size. To make informative estimation and inference feasible, we require that the overall contribution of the time varying variables after eliminating the individual specific heterogeneity can be captured by a relatively small number of the available variables whose identities are unknown. This restriction allows the problem of estimation to proceed as a variable selection problem. Importantly, we treat the individual specific heterogeneity as fixed effects which allows this heterogeneity to be related to the observed time varying variables in an unspecified way and allows that this heterogeneity may be non-zero for all individuals. Within this framework, we provide procedures that give uniformly valid inference over a fixed subset of parameters in the canonical linear fixed effects model and over coefficients on a fixed vector of endogenous variables in panel data instrumental variables models with fixed effects and many instruments. An input to developing the properties of our proposed procedures is the use of a variant of the Lasso estimator that allows for a grouped data structure where data across groups are independent and dependence within groups is unrestricted. We provide formal conditions within this structure under which the proposed Lasso variant selects a sparse model with good approximation properties. We present simulation results in support of the theoretical developments and illustrate the use of the methods in an application aimed at estimating the effect of gun prevalence on crime rates.