A0151
Title: Regression modelling under general heterogeneity
Authors: George Kapetanios - Kings College London (United Kingdom) [presenting]
Liudas Giraitis - Queen Mary University of London (United Kingdom)
Yufei Li - Kings College London (United Kingdom)
Abstract: The aim is to introduce and analyse a setting with general heterogeneity in regression modelling. It is shown that regression models with fixed or time-varying parameters can be estimated by OLS or time-varying OLS methods, respectively, for a very wide class of regressors and noises not covered by existing modelling theory. The new setting allows the development of asymptotic theory and the estimation of standard errors. The proposed robust confidence interval estimators permit a high degree of heterogeneity in regressors and noise. The estimates of robust standard errors coincide with a well-known estimator of heteroskedasticity-consistent standard errors but are applicable to more general circumstances than just the presence of heteroscedastic noise. They are easy to compute and perform well in Monte Carlo simulations. Their robustness, generality and ease of use make them ideal for applied work. A brief empirical illustration is included.
