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  "Title": "Evidence-Based Bayesian Disaggregation of Aggregate Indices",
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  "Description": "Disaggregates an observed aggregate price index into\nsectoral components with a Bayesian state-space model in which\nthe aggregate enters as a genuine observation density rather\nthan as a renormalization identity. A random-walk-with-drift\ntransition in log space (with partial pooling on the drift and\nthe innovation scale) and an estimable cross-sectional\nconcentration produce posterior draws of the sectoral indices\nwith credible intervals, suitable as multiple-imputation input\nfor downstream dynamic models. The Hamiltonian Monte Carlo\nengine follows Stan (Carpenter et al., 2017)\n<doi:10.18637/jss.v076.i01>; model comparison uses Pareto\nSmoothed Importance Sampling Leave-One-Out cross-validation\n(Vehtari, Gelman and Gabry, 2017)\n<doi:10.1007/s11222-016-9696-4>. A closed-form linear-Gaussian\nKalman/RTS smoother provides an exact, MCMC-free Bayesian\nalternative for the same aggregate evidence.",
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