Salvato in:
| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2408.13514 |
| Tags: |
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Sommario:
- We re-investigate the asymptotic properties of the traditional OLS (pooled) estimator, $\hatβ _P$, in the context of cluster dependence. The present study considers various scenarios under various restrictions on the cluster sizes and number of clusters. It is shown that $\hatβ_P$ could be inconsistent in many realistic situations. We propose a simple estimator, $\hatβ_A$ based on data averaging. The asymptotic properties of $\hatβ_A$ are studied. It is shown that $\hatβ_A$ is consistent even when $\hatβ_P$ is inconsistent. It is further shown that the proposed estimator $\hatβ_A$ is more efficient than $\hatβ_P$ in many practical scenarios. As a consequence of averaging, we show that $\hatβ_A$ retains consistency, asymptotic normality under classical measurement error problem circumventing the use of Instrumental Variables (IV). A detailed simulation study shows the efficacy of $\hatβ_A$. It is also seen that $\hatβ_A$ yields better goodness of fit.