On robust GMM estimation with applications in economics and finance

Ansgar Steland

Discussiones Mathematicae Probability and Statistics (2000)

  • Volume: 20, Issue: 1, page 63-83
  • ISSN: 1509-9423

Abstract

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Generalized Methods of Moments (GMM) estimators are a popular tool in econometrics since introduced by Hansen (1982), because this approach provides feasible solutions for many problems present in economic data where least squares or maximum likelihood methods fail when naively applied. These problems may arise in errors-in-variable regression, estimation of labor demand curves, and asset pricing in finance, which are discussed here. In this paper we study a GMM estimator for the rank modelingapproach (RMA), which analyzes the ordinal structure of a response variable. Assuming m-dependent data consistency and asymptotic normality of the proposed estimator are shown including the important case that the instruments depend on lagged regressors.Consistent estimators for the asymptotic covariance matrices are proposed. Further, the construction of minimum variance RMA-GMM estimators is discussed. Finite sample properties are studied by a small simulation study.

How to cite

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Ansgar Steland. "On robust GMM estimation with applications in economics and finance." Discussiones Mathematicae Probability and Statistics 20.1 (2000): 63-83. <http://eudml.org/doc/287664>.

@article{AnsgarSteland2000,
abstract = {Generalized Methods of Moments (GMM) estimators are a popular tool in econometrics since introduced by Hansen (1982), because this approach provides feasible solutions for many problems present in economic data where least squares or maximum likelihood methods fail when naively applied. These problems may arise in errors-in-variable regression, estimation of labor demand curves, and asset pricing in finance, which are discussed here. In this paper we study a GMM estimator for the rank modelingapproach (RMA), which analyzes the ordinal structure of a response variable. Assuming m-dependent data consistency and asymptotic normality of the proposed estimator are shown including the important case that the instruments depend on lagged regressors.Consistent estimators for the asymptotic covariance matrices are proposed. Further, the construction of minimum variance RMA-GMM estimators is discussed. Finite sample properties are studied by a small simulation study.},
author = {Ansgar Steland},
journal = {Discussiones Mathematicae Probability and Statistics},
keywords = {asset pricing; labor demand curves; nonparametric regression; rank statistics; minimum variance estimation},
language = {eng},
number = {1},
pages = {63-83},
title = {On robust GMM estimation with applications in economics and finance},
url = {http://eudml.org/doc/287664},
volume = {20},
year = {2000},
}

TY - JOUR
AU - Ansgar Steland
TI - On robust GMM estimation with applications in economics and finance
JO - Discussiones Mathematicae Probability and Statistics
PY - 2000
VL - 20
IS - 1
SP - 63
EP - 83
AB - Generalized Methods of Moments (GMM) estimators are a popular tool in econometrics since introduced by Hansen (1982), because this approach provides feasible solutions for many problems present in economic data where least squares or maximum likelihood methods fail when naively applied. These problems may arise in errors-in-variable regression, estimation of labor demand curves, and asset pricing in finance, which are discussed here. In this paper we study a GMM estimator for the rank modelingapproach (RMA), which analyzes the ordinal structure of a response variable. Assuming m-dependent data consistency and asymptotic normality of the proposed estimator are shown including the important case that the instruments depend on lagged regressors.Consistent estimators for the asymptotic covariance matrices are proposed. Further, the construction of minimum variance RMA-GMM estimators is discussed. Finite sample properties are studied by a small simulation study.
LA - eng
KW - asset pricing; labor demand curves; nonparametric regression; rank statistics; minimum variance estimation
UR - http://eudml.org/doc/287664
ER -

References

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  10. [10] M. Loeve, Probability Theory I, 4-th ed., Springer, New York 1977. Zbl0359.60001
  11. [11] J. Mossin, Security pricing and investment criteria in competitive markets, American Economic Review (December) 1966. 
  12. [12] P.K. Sen, Weak convergence of multidimensional empirical processes for stationary φ-mixing processes, Ann. Prob. 2, 1, (1974), 147-154. Zbl0276.60030
  13. [13] Serfling, Approximation Theorems of Mathematical Statistics, Wiley: New York 1980. 
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  15. [15] A. Steland, Ordinal regression based on the rank modeling approach, Arbeitsbericht Europa-Universität Frankfurt (O), 1998. 

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