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Robust likelihood ratio tests for incomplete economic models

Author

Listed:
  • Hiroaki Kaido

    (Institute for Fiscal Studies and Boston University)

  • Yi Zhang

    (Institute for Fiscal Studies)

Abstract

This study develops a framework for testing hypotheses on structural parameters in in-complete models. Such models make set-valued predictions and hence do not generally yield a unique likelihood function. The model structure, however, allows us to construct tests based on the least favorable pairs of likelihoods using the theory of Huber and Strassen (1973). We develop tests robust to model incompleteness that possess certain optimality properties. We also show that sharp identifying restrictions play a role in constructing such tests in a computationally tractable manner. A framework for analyzing the local asymptotic power of the tests is developed by embedding the least favorable pairs into a model that allows local approximations under the limits of experiments argument. Examples of the hypotheses we consider include those on the presence of strategic interaction e?ects in discrete games of complete information. Monte Carlo experiments demonstrate the robust performance of the proposed tests.

Suggested Citation

  • Hiroaki Kaido & Yi Zhang, 2019. "Robust likelihood ratio tests for incomplete economic models," CeMMAP working papers CWP68/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:68/19
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    3. Andrin Pelican & Bryan S. Graham, 2020. "An Optimal Test for Strategic Interaction in Social and Economic Network Formation between Heterogeneous Agents," NBER Working Papers 27793, National Bureau of Economic Research, Inc.
    4. Shuowen Chen & Hiroaki Kaido, 2022. "Robust Tests of Model Incompleteness in the Presence of Nuisance Parameters," Papers 2208.11281, arXiv.org.

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