Generalised Empirical Likelihood Kernel Block Bootstrapping
Paulo Parente () and
Richard J. Smith
No 2018/55, Working Papers REM from ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa
This article unveils how the kernel block bootstrap method of Parente and Smith (2018a,2018b) can be applied to make inferences on parameters of models de ned through moment restrictions. Bootstrap procedures that resort to generalised empirical likelihood implied probabilities to draw observations are also introduced. We prove the rst-order asymptotic validity of bootstrapped test statistics for overidentifying moment restrictions, parametric restrictions and additional moment restrictions. Resampling methods based on such probabilities were shown to be efficient by Brown and Newey (2002). A set of simulation experiments reveals that the statistical tests based on the proposed bootstrap methods perform better than those that rely on first-order asymptotic theory.
Keywords: Bootstrap; heteroskedastic and autocorrelation consistent inference; Generalised Method of Moments; Generalised Empirical Likelihood (search for similar items in EconPapers)
JEL-codes: C14 C15 C32 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ise:remwps:wp0552018
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