 A Bayesian Approach to Matrix Balancing: Transformation of Industry-Level Data under NACE RevisionJakub Boratyński ()
Central European Journal of Economic Modelling and Econometrics, 2016, vol. 8, issue 4, 219-239 Abstract: We apply Bayesian inference to estimate transformation matrix that converts vector of industry outputs from NACE Rev. 1.1 to NACE Rev. 2 classification. In formal terms, the studied issue is a representative of the class of matrix balancing (updating, disaggregation) problems, often arising in the field of multi-sector economic modelling. These problems are characterised by availability of only partial, limited data and a strong role for prior assumptions, and are typically solved using bi-proportional balancing or cross-entropy minimisation methods. Building on Bayesian highest posterior density formulation for a similarly structured case, we extend the model with specification of prior information based on Dirichlet distribution, as well as employ MCMC sampling. The model features a specific likelihood, representing accounting restrictions in the form of an underdetermined system of equations. The primary contribution, compared to the alternative, widespread approaches, is in providing a clear account of uncertainty. Keywords: matrix balancing; Bayesian inference; NACE revision; transformation matrix; multi-sector modelling (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:psc:journl:v:8:y:2016:i:4:p:219-239 Access Statistics for this article More articles in Central European Journal of Economic Modelling and Econometrics from Central European Journal of Economic Modelling and Econometrics |
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