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Testing Monotonicity in Unobservables with Panel Data

Author

Listed:
  • Liangjun Su

    (Singapore Management University)

  • Stefan Hoderlein

    (Boston College)

  • Halbert White

Abstract

Monotonicity in a scalar unobservable is a crucial identifying assumption for an important class of nonparametric structural models accommodating unobserved heterogeneity. Tests for this monotonicity have previously been unavailable. This paper proposes and analyzes tests for scalar monotonicity using panel data for structures with and without time-varying unobservables, either partially or fully nonseparable between observables and unobservables. Our nonparametric tests are computationally straightforward, have well behaved limiting distributions under the null, are consistent against pre- cisely specified alternatives, and have standard local power properties. We provide straightforward bootstrap methods for inference. Some Monte Carlo experiments show that, for empirically relevant sample sizes, these reasonably control the level of the test, and that our tests have useful power. We apply our tests to study asset returns and demand for ready-to-eat cereals.

Suggested Citation

  • Liangjun Su & Stefan Hoderlein & Halbert White, 2013. "Testing Monotonicity in Unobservables with Panel Data," Boston College Working Papers in Economics 892, Boston College Department of Economics, revised 01 Feb 2016.
    • Handle: RePEc:boc:bocoec:892
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    References listed on IDEAS

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    More about this item

    Keywords

    monotonicity; nonparametric; nonseparable; specification test; unobserved heterogeneity;
    All these keywords.

    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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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