Publicação
Generalised empirical likelihood Kernel Block bootstrapping
| Resumo: | 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 defined through moment restrictions. Bootstrap procedures that resort to generalised empirical likelihood implied probabilities to draw observations are also introduced. We prove the first-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 |
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| Autores principais: | Parente, Paulo M.D.C. |
| Outros Autores: | Smith, Richard J. |
| Assunto: | Bootstrap heteroskedastic and autocorrelation consistent inference Generalised Method of Moments Generalised Empirical Likelihood |
| Ano: | 2018 |
| País: | Portugal |
| Tipo de documento: | working paper |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | 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 defined through moment restrictions. Bootstrap procedures that resort to generalised empirical likelihood implied probabilities to draw observations are also introduced. We prove the first-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 |
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