Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects
含交互固定效应的高维面板数据模型：估计与推断AI 解读 ↓
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含交互固定效应的高维面板数据模型：估计与推断
- 研究问题
- 在回归变量很多且存在不可观测共同因子的面板模型中，如何扩展共同相关效应方法，实现参数估计与统计推断？
- 方法与思路
- 作者对横截面平均协变量进行降维，构造投影以消除潜在因子，再使用lasso估计和去稀疏化方法开展推断。
- 材料陈述的贡献
- 摘要称，该方法允许变量数超过总样本量，并在长面板和固定时间长度下推导收敛率及渐近正态性，另有资产定价应用。
- 阅读关注点
- 应核对投影消除因子的条件、稀疏性与横截面依赖限制，以及长面板和短面板理论、去稀疏化方差估计的区别。
- 阅读边界
- 这是arXiv预印本，同行评审及期刊接收未核实；摘要未列出正则条件和应用结果，不能断言任意高维面板均适用。
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 non-trivial 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.