This paper presents robust inference methods for general linear hypotheses in linear panel data models with latent group structure in the coefficients. We employ a selective conditional inference approach, deriving the conditional distribution of coefficient estimates given the group structure estimated from the data. Our procedure provides valid inference under possible violations of group separation, where distributional properties of group-specific coefficients remain unestablished. Furthermore, even when group separation does hold, our method demonstrates superior finite-sample properties compared to traditional asymptotic approaches. This improvement stems from our procedure's ability to account for statistical uncertainty in the estimation of group structure. We demonstrate the effectiveness of our approach through Monte Carlo simulations and apply the methods to two datasets on: (i) the relationship between income and democracy, and (ii) the cyclicality of firm-level R&D investment.

翻译：本文针对系数具有潜在分组结构的线性面板数据模型，提出了一般线性假设的稳健推断方法。我们采用选择性条件推断方法，推导出在给定从数据估计的分组结构条件下系数估计的条件分布。我们的方法在可能违反分组分离的情况下仍能提供有效的推断，此时分组特定系数的分布特性尚未确立。此外，即使分组分离确实成立，与传统渐近方法相比，我们的方法也展现出更优的有限样本性质。这一改进源于我们的方法能够考虑分组结构估计中的统计不确定性。我们通过蒙特卡洛模拟证明了所提方法的有效性，并将其应用于两个数据集：(i) 收入与民主的关系，以及 (ii) 企业层面研发投资的周期性。