UNLOCKING THE REGRESSION SPACE
提出一个能容纳回归模型中广泛异质性的框架，证明在现有理论未覆盖的条件下仍可用普通最小二乘法估计，并给出稳健标准误估计量，适用于缺失数据等场景。
This article 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 ordinary least squares (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, Econometrica 48 , 817–838) 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 article provides a brief empirical illustration.