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Bootstrap Inference Under Cross Sectional Dependence

Author

Listed:
  • Timothy Conley

    (Western University)

  • Sílvia Gonçalves

    (McGill University)

  • Min Seong Kim

    (University of Connecticut)

  • Benoit Perron

    (Université of Montréal)

Abstract

In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross sectional/spatial dependence which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent bootstrap of Shao (2010) and generates data by multiplying a vector of independently and identically distributed external variables by the eigendecomposition of a bootstrap kernel. We prove the validity of our method for studentized and unstudentized statistics under a linear array representation of the data. Simulation experiments document the potential for improved inference with our approach. We illustrate our method in a firm-level regression application investigating the relationship between firms’ sales growth and the import activity in their local markets using unique firm-level and imports data for Canada.

Suggested Citation

  • Timothy Conley & Sílvia Gonçalves & Min Seong Kim & Benoit Perron, 2022. "Bootstrap Inference Under Cross Sectional Dependence," Working papers 2022-14, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:2022-14
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    References listed on IDEAS

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    More about this item

    Keywords

    bootstrap; cross sectional dependence; spatial HAC; eigendecomposition; economic distance;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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