In this paper we consider time series models belonging to the AR(autoregressive) familiy and deal with the estimation of the residual variance. This is important because estimates of the variance enter, for example, into confidence sets for the parameters of the model, in the estimation of the spectrum , in expressions for the estimated error of prediction and in sample quantities used to make inferences about the order of the model. We consider the asymptotic biases for moment and least squares estimators of the residual variance, and compare them with known results when available and with those for maximum likelihood estimators under normality. For finite samples, simulation results are presented. Key words: AR models, bias, least squares estimator, maximum likelihood estimator, moment estimator, residual variance, time series. 1. Introduction. We consider time series models belonging to the AR(p) family in which the observable stationary process fX t g has EfX t g = ¯ and finite...