Distribution-Matching Posterior Inference for Incomplete Structural Models
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908746467246080 |
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| author | Kano, Takashi |
| author_facet | Kano, Takashi |
| contents | This paper introduces a Bayesian inference framework for incomplete structural models, termed distribution-matching posterior inference (DMPI). Extending the minimal econometric interpretation (MEI), DMPI constructs a divergence-based quasi-likelihood using the Jensen-Shannon divergence between theoretical and empirical population-moment distributions, based on a Dirichlet-multinomial structure with additive smoothing. The framework accommodates model misspecification and stochastic singularity. Posterior inference is implemented via a sequential Monte Carlo algorithm with Metropolis-Hastings mutation that jointly samples structural parameters and theoretical moment distributions. Monte Carlo experiments using misspecified New Keynesian (NK) models demonstrate that DMPI yields robust inference and improves distribution-matching coherence by probabilistically down-weighting moment distributions inconsistent with the structural model. An empirical application to U.S. data shows that a parsimonious stochastic singular NK model provides a better fit to business-cycle moments than an overparameterized full-rank counterpart. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01077 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Distribution-Matching Posterior Inference for Incomplete Structural Models Kano, Takashi Econometrics This paper introduces a Bayesian inference framework for incomplete structural models, termed distribution-matching posterior inference (DMPI). Extending the minimal econometric interpretation (MEI), DMPI constructs a divergence-based quasi-likelihood using the Jensen-Shannon divergence between theoretical and empirical population-moment distributions, based on a Dirichlet-multinomial structure with additive smoothing. The framework accommodates model misspecification and stochastic singularity. Posterior inference is implemented via a sequential Monte Carlo algorithm with Metropolis-Hastings mutation that jointly samples structural parameters and theoretical moment distributions. Monte Carlo experiments using misspecified New Keynesian (NK) models demonstrate that DMPI yields robust inference and improves distribution-matching coherence by probabilistically down-weighting moment distributions inconsistent with the structural model. An empirical application to U.S. data shows that a parsimonious stochastic singular NK model provides a better fit to business-cycle moments than an overparameterized full-rank counterpart. |
| title | Distribution-Matching Posterior Inference for Incomplete Structural Models |
| topic | Econometrics |
| url | https://arxiv.org/abs/2601.01077 |