Finite Sample Properties of the Two-Step Empirical Likelihood Estimator

2005 ◽  
Vol 24 (3) ◽  
pp. 247-263 ◽  
Author(s):  
Patrik Guggenberger ◽  
Jinyong Hahn
Keyword(s):  
Empirical Likelihood ◽  
Finite Sample ◽  
Likelihood Estimator ◽  
Finite Sample Properties

TESTING FOR NONNESTED CONDITIONAL MOMENT RESTRICTIONS VIA CONDITIONAL EMPIRICAL LIKELIHOOD

2010 ◽  
Vol 27 (1) ◽  
pp. 114-153 ◽  
Author(s):  
Taisuke Otsu ◽  
Yoon-Jae Whang
Keyword(s):  
Empirical Likelihood ◽  
Finite Sample ◽  
Conditional Moment ◽  
Null Distributions ◽  
Finite Dimensional ◽  
Global Power ◽  
Finite Sample Properties ◽  
The Moment ◽  
Moment Restrictions ◽  
Power Properties

We propose nonnested tests for competing conditional moment restriction models using the method of conditional empirical likelihood, recently developed by Kitamura, Tripathi, and Ahn (2004) and Zhang and Gijbels (2003). To define the test statistics, we use the implied conditional probabilities from conditional empirical likelihood, which take into account the full implications of conditional moment restrictions. We propose three types of nonnested tests: the moment-encompassing, Cox-type, and efficient score-encompassing tests. We derive the asymptotic null distributions and investigate their power properties against a sequence of local alternatives and a fixed global alternative. Our tests have distinct global power properties from some of the existing tests based on finite-dimensional unconditional moment restrictions. Simulation experiments show that our tests have reasonable finite sample properties and dominate some of the existing nonnested tests in terms of size-corrected powers.

scholarly journals Asymptotic Theory for Regressions with Smoothly Changing Parameters

2013 ◽  
Vol 5 (2) ◽  
pp. 133-162 ◽  
Author(s):  
Eric Hillebrand ◽  
Marcelo C. Medeiros ◽  
Junyue Xu
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Theory ◽  
Asymptotic Properties ◽  
Smooth Transition ◽  
Finite Sample ◽  
Likelihood Estimator ◽  
Output Data ◽  
Asymptotically Normal ◽  
Finite Sample Properties ◽  
Quasi Maximum Likelihood

Abstract: We derive asymptotic properties of the quasi-maximum likelihood estimator of smooth transition regressions when time is the transition variable. The consistency of the estimator and its asymptotic distribution are examined. It is shown that the estimator converges at the usual -rate and has an asymptotically normal distribution. Finite sample properties of the estimator are explored in simulations. We illustrate with an application to US inflation and output data.

Sign in / Sign up

Export Citation Format

Share Document