Estimation and inference in high‐dimensional panel data models with interactive fixed effects
交互固定效应高维面板模型的估计与推断AI 解读 ↓
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交互固定效应高维面板模型的估计与推断
- 研究问题
- 研究解释变量维数很高、甚至超过总样本量时，如何在含不可观测共同因子的面板模型中估计参数并进行有效推断。
- 方法与思路
- 作者对截面平均协变量降维，构造投影以消除因子，再应用 lasso 估计，并通过去稀疏化版本开展参数推断。
- 材料陈述的贡献
- 摘要将 CCE 思路扩展到高维情形，给出长面板与固定时间长度下的收敛率和渐近正态结果，并应用于特征资产定价。
- 阅读关注点
- 应核对因子消除的秩条件、稀疏性与调参要求，以及固定时间长度下的渐近框架和去稀疏化推断所需正则条件。
- 阅读边界
- 仅据所给 Crossref 摘要，不能推断其他版本变化；具体正则条件与模拟数值未说明，高维可估计并不意味着任意设计下均有效。
原文摘要
We develop new econometric methods for estimation and inference in high‐dimensional panel data models with interactive fixed effects. Our approach can be regarded as a nontrivial extension of the very popular common correlated effects (CCE) approach. Roughly speaking, we proceed as follows: We first construct a projection device to eliminate the unobserved factors from the model by applying a dimensionality reduction transform to the matrix of cross‐sectionally averaged covariates. The unknown parameters are then estimated by applying lasso techniques to the projected model. For inference purposes, we derive a desparsified version of our lasso‐type estimator. While the original CCE approach is restricted to the low‐dimensional case where the number of regressors is small and fixed, our methods can deal with both low‐ and high‐dimensional situations where the number of regressors is large and may even exceed the overall sample size. We derive theory for our estimation and inference methods both in the large‐ T ‐case, where the time‐series length T tends to infinity, and in the small‐ T ‐case, where T is a fixed natural number. Specifically, we derive the convergence rate of our estimator and show that its desparsified version is asymptotically normal under suitable regularity conditions. The theoretical analysis of the paper is complemented by a simulation study and an empirical application to characteristic based asset pricing.