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The Bootstrap for Network Dependent Processes

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  • Denis Kojevnikov

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.

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  • Denis Kojevnikov, 2021. "The Bootstrap for Network Dependent Processes," Papers 2101.12312, arXiv.org.
    • Handle: RePEc:arx:papers:2101.12312
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    References listed on IDEAS

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    8. Calhoun, Gray, 2014. "Block Bootstrap Consistency Under Weak Assumptions," Staff General Research Papers Archive 34313, Iowa State University, Department of Economics.
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    Cited by:

    1. Timothy G. Conley & Sílvia Gonçalves & Min Seong Kim & Benoit Perron, 2023. "Bootstrap inference under cross‐sectional dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 511-569, May.
    2. Tadao Hoshino & Takahide Yanagi, 2021. "Causal Inference with Noncompliance and Unknown Interference," Papers 2108.07455, arXiv.org, revised Oct 2023.
    3. Michael P. Leung, 2022. "Causal Inference Under Approximate Neighborhood Interference," Econometrica, Econometric Society, vol. 90(1), pages 267-293, January.
    4. Michael P. Leung, 2023. "Network Cluster‐Robust Inference," Econometrica, Econometric Society, vol. 91(2), pages 641-667, March.
    5. Han Kevin & Ugander Johan, 2023. "Model-based regression adjustment with model-free covariates for network interference," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-29, January.

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