  A weighted regression procedure is proposed for regression type problems
where the innovations are heavy-tailed. This method approximates the least
absolute regression method in large samples, and the main advantage will be if
the sample is large and for problems with many independent variables. In such
problems bootstrap methods must often be utilized to test hypotheses and
especially in such a case this procedure has an advantage over least absolute
regression. The procedure will be illustrated on first-order autoregressive
problems, including the random walk. A bootstrap procedure is used to test the
unit root hypothesis and good results were found.
