This paper introduces and analyzes a framework that accommodates general heterogeneity in regression modeling. It demonstrates that regression models with fixed or time-varying parameters can be estimated using the OLS and time-varying OLS methods, respectively, across a broad class of regressors and noise processes not covered by existing theory. The proposed setting facilitates the development of asymptotic theory and the estimation of robust standard errors. The robust confidence interval estimators accommodate substantial heterogeneity in both regressors and noise. The resulting robust standard error estimates coincide with White's (1980) heteroskedasticity-consistent estimator but are applicable to a broader range of conditions, including models with missing data. They are computationally simple and perform well in Monte Carlo simulations. Their robustness, generality, and ease of implementation make them highly suitable for empirical applications. Finally, the paper provides a brief empirical illustration.

翻译：本文提出并分析了一个能够容纳回归模型中一般异质性的框架。研究表明，对于现有理论未涵盖的广泛回归变量和噪声过程，分别可以使用普通最小二乘法（OLS）和时变OLS方法估计具有固定或时变参数的回归模型。所提出的设定有助于发展渐近理论并估计稳健标准误。稳健置信区间估计器能够适应回归变量和噪声中的显著异质性。由此得到的稳健标准误估计与White（1980）的异方差一致性估计器一致，但适用于更广泛的条件，包括存在缺失数据的模型。这些方法计算简便，在蒙特卡洛模拟中表现良好。其稳健性、普适性和易于实现的特点使其非常适合实证应用。最后，本文提供了一个简短的实证示例。