Denis Kojevnikov, Kyungchul Song
arXiv 8 Sep 2021 · Econometrics · publishedJournal of Econometrics (2023) · 1 citations (OpenAlex)
arXiv:2109.03971 · PDF · DOI · OpenAlex · Extracted main text
This paper focuses on a setting with observations having a cluster dependence structure and presents two main impossibility results. First, we show that when there is only one large cluster, i.e., the researcher does not have any knowledge on the dependence structure of the observations, it is not possible to consistently discriminate the mean. When within-cluster observations satisfy the uniform central limit theorem, we also show that a sufficient condition for consistent $\sqrt{n}$-discrimination of the mean is that we have at least two large clusters. This result shows some limitations for inference when we lack information on the dependence structure of observations. Our second result provides a necessary and sufficient condition for the cluster structure that the long run variance is consistently estimable. Our result implies that when there is at least one large cluster, the long run variance is not consistently estimable.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pötscher (2002) Lower Risk Bounds and Properties of Confidence Sets for Ill-Posed Estimation Problems with Applications to Spectral Density and… | 0.644 | 4 | 1 | 100% |
| 2 | Djogbenou, MacKinnon, and Nielsen (2019) Asymptotic Theory and Wild Bootstrap Inference with Clustered Errors | 0.585 | 3 | 1 | 100% |
| 3 | Hansen and Lee (2019) Asymptotic Theory for Clustered Samples | 0.585 | 3 | 1 | 100% |
| 4 | Ibragimov and Has'minskii (1981) Statistical Estimation: Asymptotic Theory | 0.585 | 3 | 1 | 100% |
| 5 | Ibragimov and Müller (2010) t-Statistic Based Correlation and Heterogeneity Robust Inference | 0.585 | 3 | 1 | 100% |
| 6 | Leung (2021) Dependence-Robust Inference Using Resampled Statistics | 0.585 | 3 | 1 | 100% |
| 7 | Roth, Sant'Anna, Bilinski, and Poe (2022) What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature | 0.585 | 3 | 1 | 100% |
| 8 | Song (2016) Ordering-Free Inference from Locally Dependent Data self | 0.585 | 3 | 1 | 100% |
| 9 | Aronow and Samii (2017) Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experiment | 0.511 | 2 | 1 | 100% |
| 10 | Bertanha and Moreira (2020) Impossible Inference in Econometrics: Theory and Applications | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 38 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Genuinely Robust Inference for Clustered Data | 0.511 | 2 | 2 |