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Subvector inference for Varying Coefficient Models with Partial Identification

Author

Listed:
  • Shengjie Hong
  • Yu-Chin Hsu
  • Yuanyuan Wan

Abstract

This paper develops inference methods for a general class of varying coefficient models defined by a set of moment inequalities and/or equalities, where unknown functional parameters are not necessarily point-identified. We propose an inferential procedure for a subvector of the parameters and establish the asymptotic validity of the resulting confidence sets uniformly over a broad family of data-generating processes. We also propose a specification test for the varying coefficient models considered in this paper. Monte Carlo studies show that the proposed methods work well in finite samples.

Suggested Citation

  • Shengjie Hong & Yu-Chin Hsu & Yuanyuan Wan, 2023. "Subvector inference for Varying Coefficient Models with Partial Identification," Working Papers tecipa-756, University of Toronto, Department of Economics.
    • Handle: RePEc:tor:tecipa:tecipa-756
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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