Titlebook: Robust Methods and Asymptotic Theory in Nonlinear Econometrics; Herman J. Bierens Book 1981 Springer-Verlag Berlin Heidelberg 1981 Asympto

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TRUST

敬禮

Nonlinear Regression Models,er of alternative assumptions about the distributions of errors and regressors. In section 3.1 we first discuss the least squares estimation theory of Jennrich (1969), but in a slightly more general setting than Jennrich did. Then, at the end of this section, we shall weaken some assumptions and esp

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Gnrh670

Nonlinear Models with Lagged Dependent Variables, distributed. Consequently standard laws of large numbers and central limit theorems can no longer be used for deriving the asymptotic properties of the estimators involved. It is well known that for linear regression models this difficulty can be overcome by imposing some stability conditions on th

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Some Applications,ns of robust M-estimation involve linear models. The first one originates from the research program of the Foundation for Economic Research of the University of Amsterdam, the second one comes from Christopher A. Sims, who suggested this application and kindly provided the data. The linearity of the

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Nonlinear Structural Equations,r example when the regressors are subject to errors of measurement (errors in variables) or when the equation involved is a member of a simultaneous equation system where one or more of the explanatory variables are dependent variables of other equations of the system.