Conference Agenda

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 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 resulting robust confidence interval estimators accommodate substantial heterogeneity in both regressors and noise. The robust standard error estimates coincide with White’s (1980) heteroskedasticity-consistent estimator but apply under much broader conditions, including models with missing data. The methods are computationally simple and perform well in Monte Carlo simulations, making them highly suitable for empirical applications. The paper also provides a brief empirical illustration.