论文标题
一种基于投影的交互式固定效果面板数据模型的方法
A projection based approach for interactive fixed effects panel data models
论文作者
论文摘要
本文介绍了一种基于简单的筛子方法，用于估计和推断具有交互式固定效果的面板数据模型中的回归参数。该方法的关键假设是，可以将因子负载分解为单个特征的未知平滑函数以及特质误差项。我们的估计器通过采用简单的部分最小二乘形式来提供比现有方法的优势，从而消除了对迭代程序或初步因素估计的需求。在得出渐近特性时，我们发现极限分布表现出不连续性，取决于我们的基础功能如何解释因子负载，这是由误差因子加载的方差衡量的。这一发现表明，使用估计的渐近协方差的常规``插入''方法可以产生过度保守的覆盖概率。我们证明，可以通过横截面引导法实现统一有效的非保守推理。 Monte Carlo模拟证实了估计器在平均误差方面的出色性能和引导程序的良好覆盖结果。我们通过分析经合组织国家的增长率决定因素来证明我们方法论的实际相关性。
This paper introduces a straightforward sieve-based approach for estimating and conducting inference on regression parameters in panel data models with interactive fixed effects. The method's key assumption is that factor loadings can be decomposed into an unknown smooth function of individual characteristics plus an idiosyncratic error term. Our estimator offers advantages over existing approaches by taking a simple partial least squares form, eliminating the need for iterative procedures or preliminary factor estimation. In deriving the asymptotic properties, we discover that the limiting distribution exhibits a discontinuity that depends on how well our basis functions explain the factor loadings, as measured by the variance of the error factor loadings. This finding reveals that conventional ``plug-in'' methods using the estimated asymptotic covariance can produce excessively conservative coverage probabilities. We demonstrate that uniformly valid non-conservative inference can be achieved through the cross-sectional bootstrap method. Monte Carlo simulations confirm the estimator's strong performance in terms of mean squared error and good coverage results for the bootstrap procedure. We demonstrate the practical relevance of our methodology by analyzing growth rate determinants across OECD countries.
