The second-order bias and mean squared error of nonlinear estimators. (English) Zbl 0866.62010
J. Econom. 75, No. 2, 369-395 (1996); corrigendum ibid. 124, No. 1, 203-204 (2005).
Summary: Despite the now widespread use of nonlinear estimators, their finite-sample properties have received very little attention in either the statistics or econometrics literature. We partially redress this problem by deriving and examining the second-order bias and mean squared error of a fairly wide class of nonlinear estimators which includes Nonlinear Least Squares, Maximum Likelihood, and many Generalized Method of Moments estimators as special cases. A number of examples are provided. The results from a Monte Carlo exercise demonstrate how the results can be applied for improved inferences.
MSC:
| 62F12 | Asymptotic properties of parametric estimators |
| 62P20 | Applications of statistics to economics |
| 62J02 | General nonlinear regression |
| 62J05 | Linear regression; mixed models |
Keywords:
nonlinear least squares; maximum likelihood; generalized method of moments estimators; stochastic expansions; nonlinear estimators; finite-sample properties; second-order bias; mean squared error; Monte CarloReferences:
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