In this paper, we argue that a large class of recursive contracts can be studied by means of the conventional Negishi method. A planner is responsible for prescribing current actions along with a distribution of future utility values to all agents, so as to maximize their weighted sum of utilities. Under convexity, the method yields the exact efficient frontier. Otherwise, the implementation requires contracts be contingent on publicly observable random signals uncorrelated to fundamentals. We also provide operational first-order conditions for the characterization of efficient contracts. Finally, we compare extensively our approach with the dual method established in the literature.

Bloise, G., Siconolfi, P. (2022). A Negishi Approach to Recursive Contracts. ECONOMETRICA, 90(6), 2821-2855 [10.3982/ECTA18310].

A Negishi Approach to Recursive Contracts

Bloise, G
;
2022-01-01

Abstract

In this paper, we argue that a large class of recursive contracts can be studied by means of the conventional Negishi method. A planner is responsible for prescribing current actions along with a distribution of future utility values to all agents, so as to maximize their weighted sum of utilities. Under convexity, the method yields the exact efficient frontier. Otherwise, the implementation requires contracts be contingent on publicly observable random signals uncorrelated to fundamentals. We also provide operational first-order conditions for the characterization of efficient contracts. Finally, we compare extensively our approach with the dual method established in the literature.
2022
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore SECS-P/01 - ECONOMIA POLITICA
English
Recursive contracts
efficiency
Negishi method
dynamic programming
optimal policy
Bloise, G., Siconolfi, P. (2022). A Negishi Approach to Recursive Contracts. ECONOMETRICA, 90(6), 2821-2855 [10.3982/ECTA18310].
Bloise, G; Siconolfi, P
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/320548
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