Asymptotic theory in heteroscedastic nonlinear models

Jun Shao

Statistics & Probability Letters, 1990, vol. 10, issue 1, 77-85

Abstract: Under a nonlinear regression model with heteroscedastic errors, the consistency and asymptotic normality of the least squares estimator are proved and consistent estimators of the asymptotic covariance matrix of the least squares estimator are obtained. Statistical inference methods based on these results are then asymptotically valid in both homoscedastic and heteroscedastic models.

Keywords: Consistency; asymptotic; normality; asymptotic; covariance; matrix; jackknife (search for similar items in EconPapers)
Date: 1990
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