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Implications of uncertainty for optimal policies

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  • Lensman, Todd
  • Troshkin, Maxim

Abstract

We study the implications of ambiguity for optimal fiscal policy in macro public finance environments with heterogeneous agents and private idiosyncratic shocks. We describe conditions under which ambiguity implies that it is optimal to periodically reform policies. Periodic reforms lead to simplified optimal policies that are not fully contingent on future shocks; at times they also lose dependence on the full history of past shocks. These simplified policies can be characterized without complete backward induction when the time horizon is finite. However, linear policies can be far from optimal. We also show that equilibria in decentralized versions of these economies are not generally efficient, implying a meaningful role for government provision of insurance, unlike in conventional environments with a narrower view of uncertainty.

Suggested Citation

  • Lensman, Todd & Troshkin, Maxim, 2022. "Implications of uncertainty for optimal policies," Journal of Economic Theory, Elsevier, vol. 199(C).
  • Handle: RePEc:eee:jetheo:v:199:y:2022:i:c:s0022053121000235
    DOI: 10.1016/j.jet.2021.105206
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    References listed on IDEAS

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    Cited by:

    1. Cosmin L. Ilut & Martin Schneider, 2022. "Modeling Uncertainty as Ambiguity: a Review," NBER Working Papers 29915, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    Social insurance; Redistribution; Taxation; Reforms; History dependence; Efficiency of competitive equilibria;
    All these keywords.

    JEL classification:

    • H21 - Public Economics - - Taxation, Subsidies, and Revenue - - - Efficiency; Optimal Taxation
    • H11 - Public Economics - - Structure and Scope of Government - - - Structure and Scope of Government
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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