Distribution-Matching Posterior Inference for Incomplete Structural Models

Takashi Kano

Papers from arXiv.org

Abstract: Distribution-matching posterior inference (DMPI) extends the minimal econometric interpretation to generalized posterior inference and model comparison for incomplete structural models. It combines a structural-parameter prior with exact Dirichlet-multinomial scores comparing empirical and model-implied distributions of selected population moments. Ensemble size controls structural discipline, and annealed sequential Monte Carlo computes posterior and generalized evidence. In New Keynesian Phillips Curve simulations, a stochastically singular model remains useful under moderate discipline despite failing as an exact representation. In a U.S. three-equation New Keynesian DSGE application, restoring full rank raises or lowers generalized evidence depending on the structural innovation added.

Date: 2026-01, Revised 2026-10
New Economics Papers: this item is included in nep-ecm and nep-ets
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