Robust Methods and Asymptotic Theory in Nonlinear Econometrics [electronic resource]
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Author: Bierens, Herman J
Added by: sketch
Added Date: 2016-01-12
Language: eng
Subjects: Economics, Economics, Economics
Publishers: Berlin, Heidelberg : Springer Berlin Heidelberg
Collections: folkscanomy miscellaneous, folkscanomy, additional collections
ISBN Number: 9783642455292, 3642455298
Pages Count: 600
PPI Count: 600
PDF Count: 1
Total Size: 61.97 MB
PDF Size: 16.45 MB
Extensions: djvu, gif, pdf, gz, zip, torrent, log, mrc
Downloads: 317
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Total Files: 18
Media Type: texts
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Robust Methods and Asymptotic Theory in Nonlinear Econometrics
Author: Herman J. Bierens
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-10838-2
DOI: 10.1007/978-3-642-45529-2
Table of Contents:
1 Introduction -- 1.1 Specification and misspecification of the econometric model -- 1.2 The purpose and scope of this study -- 2 Preliminary Mathematics -- 2.1 Random variables, independence, Borel measurable functions and mathematical expectation -- 2.2 Convergence of random variables and distributions -- 2.3 Uniform convergence of random functions -- 2.4 Characteristic functions, stable distributions and a central limit theorem -- 2.5 Unimodal distributions -- 3 Nonlinear Regression Models -- 3.1 Nonlinear least-squares estimation -- 3.2 A class of nonlinear robust M-estimators -- 3.3 Weighted nonlinear robust M-estimation -- 3.4 Miscellaneous notes on robust M-estimation -- 4 Nonlinear Structural Equations -- 4.1 Nonlinear two-stage least squares -- 4.2 Minimum information estimators: introduction -- 4.3 Minimum information estimators: instrumental variable and scaling parameter -- 4.4 Miscellaneous notes on minimum information estimation -- 5 Nonlinear Models with Lagged Dependent Variables -- 5.1 Stochastic stability -- 5.2 Limit theorem for stochastically stable processes -- 5.3 Dynamic nonlinear regression models and implicit structural equations -- 5.4 Remarks on the stochastic stability concept -- 6 Some Applications -- 6.1 Applications of robust M-estimation -- 6.2 An application of minimum information estimation -- References
Author: Herman J. Bierens
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-10838-2
DOI: 10.1007/978-3-642-45529-2
Table of Contents:
- Introduction
- Preliminary Mathematics
- Nonlinear Regression Models
- Nonlinear Structural Equations
- Nonlinear Models with Lagged Dependent Variables
- Some Applications
1 Introduction -- 1.1 Specification and misspecification of the econometric model -- 1.2 The purpose and scope of this study -- 2 Preliminary Mathematics -- 2.1 Random variables, independence, Borel measurable functions and mathematical expectation -- 2.2 Convergence of random variables and distributions -- 2.3 Uniform convergence of random functions -- 2.4 Characteristic functions, stable distributions and a central limit theorem -- 2.5 Unimodal distributions -- 3 Nonlinear Regression Models -- 3.1 Nonlinear least-squares estimation -- 3.2 A class of nonlinear robust M-estimators -- 3.3 Weighted nonlinear robust M-estimation -- 3.4 Miscellaneous notes on robust M-estimation -- 4 Nonlinear Structural Equations -- 4.1 Nonlinear two-stage least squares -- 4.2 Minimum information estimators: introduction -- 4.3 Minimum information estimators: instrumental variable and scaling parameter -- 4.4 Miscellaneous notes on minimum information estimation -- 5 Nonlinear Models with Lagged Dependent Variables -- 5.1 Stochastic stability -- 5.2 Limit theorem for stochastically stable processes -- 5.3 Dynamic nonlinear regression models and implicit structural equations -- 5.4 Remarks on the stochastic stability concept -- 6 Some Applications -- 6.1 Applications of robust M-estimation -- 6.2 An application of minimum information estimation -- References
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