A class of partially adaptive one-step M-estimators for a nonlinear regression model with dependent observations

DOI10.1016/0304-4076(86)90039-4zbMath0613.62088OpenAlexW2139766996MaRDI QIDQ1819506

Benedikt M. Pötscher, Ingmar R. Prucha

Publication date: 1986

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4076(86)90039-4

zbMATH Keywords

asymptotically efficientdependent observationspartially adaptive one-step M-estimatorsStudent-\(t\) maximum-likelihood estimator

Mathematics Subject Classification ID

Robustness and adaptive procedures (parametric inference) (62F35) General nonlinear regression (62J02)

Related Items

Strong convergence of estimators in nonlinear autoregressive models, HAC estimation in a spatial framework, Partially adaptive estimation via a normal mixture, Basic structure of the asymptotic theory in dynamic nonlineaerco nometric models, part i: consistency and approximation concepts, Basic structure of the asymptotic theory in dynamic nonlinear econometric models, Asymptotic properties of one-step M-estimators, Asymptotic Properties of One-Step Weighted $M$-Estimators with Applications to Regression, On the robustness of two alternatives to least squares: A Monte Carlo study, A note on partially adaptive estimation via a normal mixture, On robust testing for conditional heteroscedasticity in time series models, Efficient estimation of the semiparametric spatial autoregressive model, Linearity testing for fuzzy rule-based models, On consistent statistical procedures in regression, On the formulation of uniform laws of large numbers: a truncation approach, Generic uniform convergence and equicontinuity concepts for random functions. An exploration of the basic structure, Partially adaptive estimation of nonlinear models via a normal mixture, Partially adaptive estimation of autoregressive processes via a normal mixture

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