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The Bootstrap for Network Dependent Processes

Denis Kojevnikov

arXiv 28 Jan 2021 · Econometrics · 2 citations (OpenAlex)

arXiv:2101.12312 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper focuses on the bootstrap for network dependent processes under the conditional $\psi$-weak dependence. Such processes are distinct from other forms of random fields studied in the statistics and econometrics literature so that the existing bootstrap methods cannot be applied directly. We propose a block-based approach and a modification of the dependent wild bootstrap for constructing confidence sets for the mean of a network dependent process. In addition, we establish the consistency of these methods for the smooth function model and provide the bootstrap alternatives to the network heteroskedasticity-autocorrelation consistent (HAC) variance estimator. We find that the modified dependent wild bootstrap and the corresponding variance estimator are consistent under weaker conditions relative to the block-based method, which makes the former approach preferable for practical implementation.

Citation extraction

45
references
65
in-text mentions
45
distinct cited
1
self-citations
11,059
main-text words

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Kojevnikov, D., Marmer, V., Song, K (2020) Limit theorems for network dependent random variables self0.94112483%
2Lahiri, S. N (2003) Resampling Methods for Dependent Data0.92843100%
3Shao, X (2010) The dependent wild bootstrap0.73732100%
4Conley, T. G (1999) GMM estimation with cross-sectional dependence0.64422100%
5Doukhan, P., Louhichi, S (1999) A new weak dependence condition and applications to moment inequalities0.64422100%
6Belyaev, Y., Sjöstedt-de Luna, S (2000) Weakly approaching sequences of random distributions0.40511100%
7Bentkus, V (2003) On the dependence of the Berry-Esseen bound on dimension0.40511100%
8Berti, P., Pratelli, L., Rigo, P (2006) Almost sure weak convergence of random probability measures0.40511100%
9Bühlmann, P. L (1993) The blockwise bootstrap in time series and empirical processes0.40511100%
10Calhoun, G (2018) Block bootstrap consistency under weak assumptions0.40511100%

Showing the top 10 of 45 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Graph Neural Networks for Causal Inference Under Network Confounding1.00053
2Normal Approximation for U-Statistics with Cross-Sectional Dependence0.84333
3Inference in Models of Discrete Choice with Social Interactions Using Network Data0.73732
4Causal Inference Under Approximate Neighborhood Interference0.64422
5Network Cluster-Robust Inference0.64422
6Evaluating Policy Effects under Network Interference without Network Information: A Transfer Learning Approach0.64422
7The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment0.40511
8Optimal Estimation Methodologies for Panel Data Regression Models0.40511
9Limit Theorems for Network Data without Metric Structure0.40511
10Coupling and Maximal Inequalities for Graph-Dependent Empirical Processes0.40511