Bias in nonlinear regression model with heteroscedastic or Ar(1) error structure

Chih-Ling Tsai

Statistics & Probability Letters, 1989, vol. 8, issue 2, 167-170

Abstract: We investigate the biases of the maximum likelihood estimators from normal nonlinear regression models. Emphasis is placed on the heteroscedastic and first order autoregressive error structure. Bias reduction after the parameter transformation is also discussed.

Keywords: autocorrelation; bias; heteroscedasticity; transformation (search for similar items in EconPapers)
Date: 1989
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