arXiv 6 Feb 2020 · Econometrics · publishedJournal of Applied Econometrics (2021) · 3 citations (OpenAlex)
arXiv:2002.02097 · PDF · DOI · OpenAlex · Extracted main text
We develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resampling in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This holds for regular estimators under many well-known forms of weak dependence and justifies the claim of dependence-robustness. We consider applications to settings with unknown or complicated forms of dependence, with various forms of network dependence as leading examples. We develop tests for both moment equalities and inequalities.
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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 | Song (2016) Ordering-Free Inference from Locally Dependent Data | 1.000 | 8 | 3 | 100% |
| 2 | Klaus, Yu and Plenz (2011) Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches | 0.737 | 3 | 2 | 100% |
| 3 | Jackson and Rogers (2007) Meeting Strangers and Friends of Friends: How Random Are Social Networks? | 0.693 | 5 | 1 | 100% |
| 4 | Barabási and Albert (1999) Emergence of Scaling in Random Networks | 0.644 | 2 | 2 | 100% |
| 5 | Chandrasekhar and Lewis (2016) Econometrics of Sampled Networks | 0.644 | 2 | 2 | 100% |
| 6 | Clauset, Shalizi and Newman (2009) Power-Law Distributions in Empirical Data | 0.644 | 2 | 2 | 100% |
| 7 | Gabaix (2009) Power Laws in Economics and Finance | 0.644 | 2 | 2 | 100% |
| 8 | Leung and Moon (2021) Normal Approximation in Large Network Models self | 0.644 | 2 | 2 | 100% |
| 9 | Newman (2005) Power laws, Pareto distributions and Zipf's law | 0.644 | 2 | 2 | 100% |
| 10 | Sheng (2020) A Structural Econometric Analysis of Network Formation Games Through Subnetworks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Normal Approximation in Large Network Models | 0.874 | 6 | 2 |
| 2 | Inference with few treated units | 0.830 | 7 | 2 |
| 3 | GMM and M Estimation under Network Dependence | 0.644 | 2 | 2 |
| 4 | 2109.03971 | 0.585 | 3 | 1 |
| 5 | Spillovers of Program Benefits with Missing Network Links | 0.405 | 1 | 1 |