This papers studies and compares the asymptotic bias of GMM and generalized empirical likelihood (GEL) estimators in the presence of estimated nuisance parameters. We consider cases in which the nuisance parameter is estimated from independent and identical samples. A simulation experiment is conducted for covariance structure models. Empirical likelihood offers much reduced mean and median bias, root mean squared error and mean absolute error, as compared with two-step GMM and other GEL methods. Both analytical and bootstrap bias-adjusted two-step GMM estimators are compared. Analytical bias-adjustment appears to be a serious competitor to bootstrap methods in terms of finite sample bias, root mean squared error and mean absolute error. Finite sample variance seems to be little affected.
Authors
Whitney K. Newey
Joaquim J. S. Ramalho Ramalho
Working Paper details
- DOI
- 10.1920/wp.cem.2003.0503
- Publisher
- Institute for Fiscal Studies
Suggested citation
Newey, W and Ramalho, J. (2003). Asymptotic bias for GMM and GEL estimators with estimated nuisance parameters. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/asymptotic-bias-gmm-and-gel-estimators-estimated-nuisance-parameters (accessed: 25 April 2024).
More from IFS
Understand this issue
Gender norms, violence and adolescent girls’ trajectories: Evidence from India
24 October 2022
Public investment: what you need to know
25 April 2024
The £600 billion problem awaiting the next government
25 April 2024
Policy analysis
ABC of SV: Limited Information Likelihood Inference in Stochastic Volatility Jump-Diffusion Models
We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Compu- tation which build likelihoods based on limited information.
12 August 2014
Assessing the economic benefits of education: reconciling microeconomic and macroeconomic approaches
This CAYT report discusses the strengths and limitations of several approaches to assessing the effect of education on productivity.
14 March 2013
Misreported schooling, multiple measures and returns to educational qualifications
We provide a number of contributions of policy, practical and methodological interest to the study of the returns to educational qualifications in the presence of misreporting.
1 February 2012
Academic research
Understanding Society: minimising selection biases in data collection using mobile apps
2 February 2024
Robust analysis of short panels
8 January 2024
A coefficient of variation for ordered categorical data: Analyzing relative health inequality and ageing in the UK and relative human resource inequality and gender in Canada
21 December 2023