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An augmented first-order approach for incentive problems

Author

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  • Philipp Renner

Abstract

Incentive constraints are constraints that are optimization problems themselves. If these problems are non convex then the first order approach fails. We propose an alternative solution method where we use the value function as an additional constraint. This ensures that all solutions are incentive compatible. To get the value function we use a function interpolator like sparse grids. We demonstrate our approach by solving two examples from the literature were it was shown that the first order approach fails.

Suggested Citation

  • Philipp Renner, 2020. "An augmented first-order approach for incentive problems," Working Papers 297498586, Lancaster University Management School, Economics Department.
    • Handle: RePEc:lan:wpaper:297498586
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    References listed on IDEAS

    as
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    5. Arpad Abraham & Nicola Pavoni, 2008. "Efficient Allocations with Moral Hazard and Hidden Borrowing and Lending: A Recursive Formulation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 11(4), pages 781-803, October.
    6. Johannes Brumm & Simon Scheidegger, 2017. "Using Adaptive Sparse Grids to Solve High‐Dimensional Dynamic Models," Econometrica, Econometric Society, vol. 85, pages 1575-1612, September.
    7. J. A. Mirrlees, 1999. "The Theory of Moral Hazard and Unobservable Behaviour: Part I," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(1), pages 3-21.
    Full references (including those not matched with items on IDEAS)

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