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Consistent Maximum Likelihood Estimation Of The Nonlinear Regression Model With Normal Errors

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  • Heijmans, Risto
  • Magnus, Jan

Abstract

Standard consistency proofs of the maximum likelihood estimator rely on the assumption that the observations are independent and identically distributed. In econometric models, however, this assumption is seldom satisfied. In this paper we prove consistency of the maximum likelihood estimator obtained from observations (not necessarily independent or identically distributed), whose joint distribution is known to be normal. This contains the nonlinear regression model with normal errors as a special case. Our regularity conditions appear to be mild; in particular, no uniform convergence assumption is made. An example (first-order autocorrelation) demonstrates the easy applicability of our conditions.

Suggested Citation

  • Heijmans, Risto & Magnus, Jan, 1983. "Consistent Maximum Likelihood Estimation Of The Nonlinear Regression Model With Normal Errors," University of Amsterdam, Actuarial Science and Econometrics Archive 293069, University of Amsterdam, Faculty of Economics and Business.
    • Handle: RePEc:ags:amstas:293069
    DOI: 10.22004/ag.econ.293069
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    Research Methods/ Statistical Methods;

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