Modeling asymmetric relationships is an emerging subject of interest among sociologists. York and Light advanced a method to estimate asymmetric models with panel data, which was further developed by Allison. However, little attention has been given to the large-N, large-T case, wherein autoregression, slope heterogeneity, and cross-sectional dependence are important issues to consider. The authors fill this gap by conducting Monte Carlo experiments comparing the bias and power of the fixed-effects estimator to a set of heterogeneous panel estimators. The authors find that dynamic misspecification can produce substantial biases in the coefficients. Furthermore, even when the dynamics are correctly specified, the fixed-effects estimator will produce inconsistent and unstable estimates of the long-run effects in the presence of slope heterogeneity. The authors demonstrate these findings by testing for directional asymmetry in the economic development–CO₂ emissions relationship, a key question in macro sociology, using data for 66 countries from 1971 to 2015. The authors conclude with a set of methodological recommendations on modeling directional asymmetry.

对不对称关系建模是社会学家感兴趣的新兴主题。York 和 Light 提出了一种使用面板数据估计非对称模型的方法，该方法由 Allison 进一步开发。然而，对大N、大T 的关注很少在这种情况下，自回归、斜率异质性和横截面依赖性是需要考虑的重要问题。作者通过进行蒙特卡罗实验来填补这一空白，将固定效应估计量的偏差和功效与一组异质面板估计量进行比较。作者发现动态错误指定会在系数中产生大量偏差。此外，即使正确指定了动态，固定效应估计量也会在存在斜率异质性的情况下对长期效应产生不一致和不稳定的估计。作者通过测试经济发展中的方向不对称性来证明这些发现——CO ₂ 排放关系是宏观社会学中的一个关键问题，使用 1971 年至 2015 年 66 个国家的数据。作者最后提出了一套关于建模方向不对称的方法论建议。