Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

5 Issues per year


IMPACT FACTOR 2016: 0.649

CiteScore 2016: 0.63

SCImago Journal Rank (SJR) 2016: 0.546
Source Normalized Impact per Paper (SNIP) 2016: 0.793

Mathematical Citation Quotient (MCQ) 2016: 0.03

Online
ISSN
1558-3708
See all formats and pricing
More options …
Volume 14, Issue 3 (May 2010)

Issues

First and Second Order Asymptotic Bias Correction of Nonlinear Estimators in a Non-Parametric Setting and an Application to the Smoothed Maximum Score Estimator

Emma M Iglesias
Published Online: 2010-05-11 | DOI: https://doi.org/10.2202/1558-3708.1736

This paper derives, extending the work of Rilstone, Srivastava and Ullah (1996), an analytical expression that takes account of first and second order asymptotic bias of nonlinear estimators in a non-parametric setting. By using moment expansions, we obtain a first and a second order bias removal mechanism. We specialize our results on the smoothed maximum score estimator of the coefficient vector of a binary response model in the dynamic setting of De Jong and Woutersen (2009). First order asymptotic theory has already been provided, although very large samples are needed to reach the asymptotic outcome of normality in this model. We provide a second order asymptotic expansion and, with the appropriate estimated quantities, we design a new bias-corrected estimator. Finally, a simulation study shows the advantages of our proposed bias-correction procedure.

This article offers supplementary material which is provided at the end of the article.

About the article

Published Online: 2010-05-11


Citation Information: Studies in Nonlinear Dynamics & Econometrics, ISSN (Online) 1558-3708, DOI: https://doi.org/10.2202/1558-3708.1736.

Export Citation

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

Supplementary Article Materials

Comments (0)

Please log in or register to comment.
Log in