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A Bayesian Approach to Matrix Balancing: Transformation of Industry-Level Data under NACE Revision

Jakub Boratyński ()
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Jakub Boratyński: University of Łódź

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)
JEL-codes: C11 C81 D57 C67 C68 (search for similar items in EconPapers)
Date: 2016
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Handle: RePEc:psc:journl:v:8:y:2016:i:4:p:219-239