We consider some asymptotic distribution theory for M-estimators of the parameters of a linear model whose errors are non-negative; these estimators are the solutions of constrained optimization problems and their asymptotic theory is non-standard. Under weak conditions on the distribution of the...

In recent years, a number of authors have considered extensions of classical unit root tests to cases where the process is driven by infinite variance innovations, as well as considering their asymptotic properties. Unfortunately, these extensions are typically inefficient as they do not exploit...

This paper considers the asymptotic behavior of <italic>M</italic>-estimates in a dynamic linear regression model where the errors have infinite second moments but the exogenous regressors satisfy the standard assumptions. It is shown that under certain conditions, the estimates of the parameters corresponding...

We consider some asymptotic distribution theory for M-estimators of the parameters of a linear model whose errors are non-negative; these estimators are the solutions of constrained optimization problems and their asymptotic theory is non-standard. Under weak conditions on the distribution of the...

We consider the limiting distributions of <italic>M</italic>-estimates of an “autoregressive” parameter when the observations come from an integrated linear process with infinite variance innovations. It is shown that <italic>M</italic>-estimates are, asymptotically, infinitely more efficient than the least-squares estimator...