An Information-Theoretic Approach to Partially Identified Problems

Amos Golan and Jeffrey Perloff

No 20205-009, Working Papers from Human Capital and Economic Opportunity Working Group

Abstract: An information-theoretic maximum entropy (ME) model provides an alternative approach to finding solutions to partially identified models. In these models, we can identify only a solution set rather than point-identifying the parameters of interest, given our limited information. Manski (2021) proposed using statistical decision functions in general, and the minimax-regret (MMR) criterion in particular, to choose a unique solution. Using Manski's simulations for a missing data and a treatment problem, including an empirical example, we show that ME performs the same or better than MMR. In additional simulations, ME dominates various other statistical decision functions. ME has an axiomatic underpinning and is computationally efficient.

Keywords: information theory; maximum entropy; minimax regret; statistical decision function (search for similar items in EconPapers)
JEL-codes: C15 C44 D81 (search for similar items in EconPapers)
Date: 2025-10
New Economics Papers: this item is included in nep-ecm
Note: MIP
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