Abstract
Iterated weighted least squares (IWLS) is investigated for estimating the regression coefficients in a linear model with symmetrically distributed errors. The variances of the errors are not specified; it is neither assumed that they are unknown functions of the explanatory variables nor that they are given in some parametric way IWLS is carried out in a random number of steps, of which the first one is OLS. In each step the error variance at time t is estimated with a weighted sum of m squared residuals in the neighbourhood of t and the coefficients are estimated using WLS. Furthermore an estimate of the covariance matrix is obtained. If this matrix is somehow smaller than the one before, a new step is carried out unless an upper bound has been reached. Large sample properties of IWLS are derived for fixed m. Some particular cases show that the asymptotic efficiency can be increased by allowing more than two steps. For a particular example some finite-sample properties are evaluated on the basis of simul...
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