The papers collected in the two volumes Nonlinear Models focus on the asymptotic theory of parameter estimators of nonlinear single equation models and systems of nonlinear models, in particular weak and strong consistency, asymptotic normality, and parameter inference, for cross-sections as well as for time series. A selection of papers on testing for, and estimation and inference under, model misspecification is also included. The models under review are parametric, hence their functional form is assured to be known up to ...
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The papers collected in the two volumes Nonlinear Models focus on the asymptotic theory of parameter estimators of nonlinear single equation models and systems of nonlinear models, in particular weak and strong consistency, asymptotic normality, and parameter inference, for cross-sections as well as for time series. A selection of papers on testing for, and estimation and inference under, model misspecification is also included. The models under review are parametric, hence their functional form is assured to be known up to a vector of unknown parameters, and the functional form involved is nonlinear in at least one of the parameters. The selection of earlier articles on nonlinear parametric models is extensive and, although they are not all equally influential, each has played a significant part in the development of the field. The more recent articles have been selected on the basis of their potential importance for the further development of this sphere of study.
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