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Testing for Stochastic Dominance in Social Networks

Author

Listed:
  • Firmin Doko Tchatoka

    (School of Economics, University of Adelaide)

  • Robert Garrard

    (School of Economics, University of Adelaide)

  • Virginie Masson

    (School of Economics, University of Adelaide)

Abstract

This paper illustrates how stochastic dominance criteria can be used to rank social networks in terms of efficiency, and develops statistical inference procedures for assessing these criteria. The tests proposed can be viewed as extensions of a Pearson goodness-of-fit test and a studentized maximum modulus test often used to partially rank income distributions and inequality measures. We establish uniform convergence of the empirical size of the tests to the nominal level, and show their consistency under the usual conditions that guarantee the validity of the approximation of a multinomial distribution to a Gaussian distribution. Furthermore, we propose a bootstrap method that enhances the finite-sample properties of the tests. The performance of the tests is illustrated via Monte Carlo experiments and an empirical application to risk sharing networks in rural India.

Suggested Citation

  • Firmin Doko Tchatoka & Robert Garrard & Virginie Masson, 2017. "Testing for Stochastic Dominance in Social Networks," School of Economics Working Papers 2017-02, University of Adelaide, School of Economics.
  • Handle: RePEc:adl:wpaper:2017-02
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    File URL: https://media.adelaide.edu.au/economics/papers/doc/wp2017-02.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Networks; Tests of stochastic dominance; Bootstrap; Uniform convergence.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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