We develop a nonparametric, kernel-based joint estimator for conditional mean and covariance matrices in large and unbalanced panels. The estimator is supported by rigorous consistency results and finite-sample guarantees, ensuring its reliability for empirical applications. We apply it to an extensive panel of monthly US stock excess returns from 1962 to 2021, using macroeconomic and firm-specific covariates as conditioning variables. The estimator effectively captures time-varying cross-sectional dependencies, demonstrating robust statistical and economic performance. We find that idiosyncratic risk explains, on average, more than 75% of the cross-sectional variance.

翻译：本文针对大规模非平衡面板数据，提出了一种基于核函数的非参数联合估计方法，用于估计条件均值与协方差矩阵。该估计量具备严格的一致性理论支撑与有限样本性能保证，确保了其在实证应用中的可靠性。我们将其应用于1962年至2021年期间美国股票月度超额收益的大规模面板数据，以宏观经济变量与公司层面特征作为条件变量。该估计量能有效捕捉时变的横截面相依性，展现出稳健的统计性能与经济意义。研究发现，特质风险平均可解释超过75%的横截面方差。