Estimating linear models with ordinal qualitative regressors

Abstract The theory and application of ordinal qualitative dependent variable models have been given considerable attention in the social science and statistical literatures. Linear models with ordinal qualitative regressors have, however, been neglected. In this paper a simple specification for such models is developed and a consistent asymptotically normal estimator is offered. The estimator is compared to the conventional dummy variable approach using simulated data. The simulation results indicate that substantial gains with regard to bias and efficiency can be achieved relative to estimation using a conventional dummy variable scheme. Said gains appear to increase as the number of ordinal categories increases. The asymptotic properties of the estimator are detailed in the appendices.