Robust small sample accurate inference in moment condition models

Serigne N. Lô and Elvezio Ronchetti

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3182-3197

Abstract: Procedures based on the Generalized Method of Moments (GMM) are basic tools in modern econometrics. In most cases, the theory available for making inference with these procedures is based on first order asymptotic theory. It is well-known that the (first order) asymptotic distribution does not provide accurate p-values and confidence intervals in moderate to small samples. Moreover, in the presence of small deviations from the assumed model, p-values and confidence intervals based on classical GMM procedures can be drastically affected (nonrobustness). Several alternative techniques have been proposed to improve the accuracy of GMM procedures. These alternatives address either the first order accuracy of the approximations (information and entropy econometrics (IEE)) or the nonrobustness (Robust GMM estimators and tests). A new procedure which combines robustness properties and accuracy in small samples is proposed. Specifically, IEE techniques are combined with robust methods obtained by bounding the original orthogonality function. This leads to new robust estimators and tests in moment condition models with excellent finite sample accuracy. Finally, the accuracy of the new statistic is illustrated with Monte Carlo simulations for three models on overidentifying moment conditions.

Keywords: Exponential tilting; Generalized method of moments; Information and entropy econometrics; Monte Carlo; Robust tests; Saddlepoint techniques (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947311000399
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3182-3197

DOI: 10.1016/j.csda.2011.01.020

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().