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Export Reference (APA) 
Ramalho, J. J. S. (2005). Small sample bias of alternative estimation methods for moment condition models: Monte Carlo evidence for covariance structures. Studies in Nonlinear Dynamics and Econometrics. 9 (1)
Export Reference (IEEE) 
J. J. Ramalho,  "Small sample bias of alternative estimation methods for moment condition models: Monte Carlo evidence for covariance structures", in Studies in Nonlinear Dynamics and Econometrics, vol. 9, no. 1, 2005
Export BibTeX 
@article{ramalho2005_1769467948380,
	author = "Ramalho, J. J. S.",
	title = "Small sample bias of alternative estimation methods for moment condition models: Monte Carlo evidence for covariance structures",
	journal = "Studies in Nonlinear Dynamics and Econometrics",
	year = "2005",
	volume = "9",
	number = "1",
	doi = "10.2202/1558-3708.1202",
	url = "https://www.degruyter.com/view/j/snde.2005.9.1/snde.2005.9.1.1202/snde.2005.9.1.1202.xml"
}
Export RIS 
TY  - JOUR
TI  - Small sample bias of alternative estimation methods for moment condition models: Monte Carlo evidence for covariance structures
T2  - Studies in Nonlinear Dynamics and Econometrics
VL  - 9
IS  - 1
AU  - Ramalho, J. J. S.
PY  - 2005
SN  - 1558-3708
DO  - 10.2202/1558-3708.1202
UR  - https://www.degruyter.com/view/j/snde.2005.9.1/snde.2005.9.1.1202/snde.2005.9.1.1202.xml
AB  - It is now widely recognized that the most commonly used efficient two-step GMM estimator may have large bias in small samples. In this paper we analyze by simulation the finite sample bias of two classes of alternative estimators. The first includes estimators which are asymptotically first-order equivalent to the GMM estimator, namely the continuous-updating, exponential tilting, and empirical likelihood estimators. Analytical and bootstrap bias-adjusted GMM estimators form the second class of alternatives. The Monte Carlo simulation study conducted in the paper for covariance structure models shows that all alternative estimators offer much reduced bias as compared to the GMM estimator, particularly the empirical likelihood and some of the bias-corrected GMM estimators.
ER  -