Nonparametric maximum likelihood estimators for AR and MA time series Academic Article uri icon


  • The problem of estimating the parameters of moving average or autoregressive time series is studied when the error distribution is completely unknown. Four nonparametric maximum likelihood estimators (NPMLE) are presented for this purpose. These estimators are compared with the classical moment and least squares estimators in a simulation study. The behavior of these NPMLEs is much better than the classical ones, suggesting that they should be used extensively when no parametric information is known in advance about the error distribution. An application of these estimators to coal mining accidents data is also included.

published proceedings


author list (cited authors)

  • Cao, R., Hart, J. D., & Saavedra, A.

citation count

  • 12

complete list of authors

  • Cao, R||Hart, JD||Saavedra, A

publication date

  • January 2003