On robust GMM estimation with applications in economics and finance

• Autorzy: Ansgar Steland
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

asset pricing; labor demand curves; nonparametric regression; rank statistics; minimum variance estimation

Kategorie tematyczne

• 62G05: Estimation
• 62G10: Hypothesis testing

• Wydawca: Faculty of Mathematics, Computer Science and Econometrics, University of Zielona Góra
• Czasopismo: Discussiones Mathematicae Probability and Statistics
• Rocznik: 2000
• Tom: 20
• Numer: 1
• Strony: 63-83

Daty

wydano

2000

otrzymano

1998-11-26

poprawiono

1999-10-29

Twórcy

autor

Ansgar Steland

• Europeau University Frankfurt (Oder), D-15207 Frankfurt (Oder) Germany

Bibliografia

• [1] T. Amemiya, Qualitative response models: a survey, Journal of Economic Literature, 19, (1981), 1483-1536.
• [2] T. Amemiya, Advanced Econometrics, Harvard University Press, Cambridge 1985.
• [3] M. Denker, Asymptotic Distribution Theory in Nonparametric Statistics, Advanced Lectures, Vieweg: Braunschweig 1985.
• [4] L.T. Fernholz, Von Mises Calculus for Differentiable Statistical Functionals, Volume 19 of Lecture Notes in Statistics. Springer 1983.
• [5] P. Gaenssler and W. Stute, Seminar on Empirical Processes, DMV Seminar, Birkhaeuser 1987.
• [6] R.D. Gill, Non- and semi-parametric maximum likelihood estimators and the von Mises method, Scand. J. Statist. 16 (1989), 97-128.
• [7] L.P. Hansen, Large sample properties of generalized method of moments estimators, Econometrica 50 (1982), 1031-1054.
• [8] J. Johnston and J. DiNardo, Econometric Methods, McGraw Hill: New York 1997.
• [9] J. Lintner, The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets, Review of Economic, and Statistics (Spring) 1965.
• [10] M. Loeve, Probability Theory I, 4-th ed., Springer, New York 1977.
• [11] J. Mossin, Security pricing and investment criteria in competitive markets, American Economic Review (December) 1966.
• [12] P.K. Sen, Weak convergence of multidimensional empirical processes for stationary φ-mixing processes, Ann. Prob. 2, 1, (1974), 147-154.
• [13] Serfling, Approximation Theorems of Mathematical Statistics, Wiley: New York 1980.
• [14] W.F. Sharpe, Capital asset prices: a theory of market equilibrium under conditions of risk, Journal of Finance 19 (1964), 425-442.
• [15] A. Steland, Ordinal regression based on the rank modeling approach, Arbeitsbericht Europa-Universität Frankfurt (O), 1998.

Identyfikatory

DOI

10.7151/dmps.1004