MAXIMUM LIKELIHOOD ESTIMATION FOR AUTOREGRESSIVE PROCESSES DISTURBED BY A MOVING AVERAGE

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Abstract

Abstract. Maximum likelihood estimation for stationary autoregressive processes when the signal is subject to a moving‐average sampling error is discussed. A modified maximum likelihood estimator is proposed. An algorithm for computing derivatives of the modified likelihood is suggested. Maximum likelihood estimators of the parameter vector are shown to be strongly consistent and to have a multivariate normal limiting distribution. A Monte Carlo simulation shows that the modified maximum likelihood estimator performs better than other available estimators. US current labour force data are analysed as an example.

Original languageEnglish
Pages (from-to)629-643
Number of pages15
JournalJournal of Time Series Analysis
Volume14
Issue number6
DOIs
StatePublished - Nov 1993

Keywords

  • asymptotic normality
  • Autoregressive process
  • consistency
  • maximum likelihood estimator
  • sampling error

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