Title: Probabilistic, Bayesian updating of input-output tables: Application to WIOD
Authors: Vladimir Potashnikov - RANEPA (Russia) [presenting]
Oleg Lugovoy - RANEPA (Russia)
Andrey Polbin - Russian Presidential Academy of National Economy and Public Administration (Russia)
Abstract: Developments and applicationa of probabilistic method(s) for updating IO tables are shown. The core of the methodology is a Bayesian framework which combines an information from observed data, additional believes (priors), and related uncertainties into posterior joint distribution of input-output table (IOT) coefficients. The framework can be applied to various IOT problems, including updating, disaggregation, evaluation of uncertainties in the data, and addressing incomplete/missing observations. The flexibility of the methodology is partially based on sampling techniques. We apply modern Monte Carlo Markov Chains (MCMC) methods to explore posterior distribution of IOT coefficients. We also compare results with mainstream methods of updating IOT to investigate its performance. Various indicators of performance and application to various data suggest different results. The overall performance of the method is similar or comparable with mainstream techniques. The main advantage the proposed methodology is an estimation of full profile of joint probability distribution of unknown IOT matrices. The method can be also combined with any other techniques through prior information.