Ebook: Robust Methods and Asymptotic Theory in Nonlinear Econometrics
Author: Herman J. Bierens (auth.)
- Tags: Economic Theory, Statistics for Business/Economics/Mathematical Finance/Insurance
- Series: Lecture Notes in Economics and Mathematical Systems 192
- Year: 1981
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the data. The estimation methods involved are nonlinear least squares estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non linear weighted robust M-estimation (NLWRME) for the regression case and nonlinear two-stage least squares estimation (NL2SLSE) and a new method called minimum information estimation (MIE) for the case of structural equations. The asymptotic properties of the NLLSE and the two robust M-estimation methods are derived from further elaborations of results of Jennrich. Special attention is payed to the comparison of the asymptotic efficiency of NLLSE and NLRME. It is shown that if the tails of the error distribution are fatter than those of the normal distribution NLRME is more efficient than NLLSE. The NLWRME method is appropriate if the distributions of both the errors and the regressors have fat tails. This study also improves and extends the NL2SLSE theory of Amemiya. The method involved is a variant of the instrumental variables method, requiring at least as many instrumental variables as parameters to be estimated. The new MIE method requires less instrumental variables. Asymptotic normality can be derived by employing only one instrumental variable and consistency can even be proved with out using any instrumental variables at all.
Content:
Front Matter....Pages N2-IX
Introduction....Pages 1-5
Preliminary Mathematics....Pages 6-50
Nonlinear Regression Models....Pages 51-105
Nonlinear Structural Equations....Pages 106-147
Nonlinear Models with Lagged Dependent Variables....Pages 148-176
Some Applications....Pages 177-194
Back Matter....Pages 195-203
Content:
Front Matter....Pages N2-IX
Introduction....Pages 1-5
Preliminary Mathematics....Pages 6-50
Nonlinear Regression Models....Pages 51-105
Nonlinear Structural Equations....Pages 106-147
Nonlinear Models with Lagged Dependent Variables....Pages 148-176
Some Applications....Pages 177-194
Back Matter....Pages 195-203
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