Bayesian Integration of Large Scale SNA Data Frameworks with an Application to Guatemala

J.W. Van Tongeren, J.R. Magnus

Research output: Working paperDiscussion paperOther research output

329 Downloads (Pure)


We present a Bayesian estimation method applied to an extended set of national accounts data and estimates of approximately 2500 variables. The method is based on conventional national accounts frameworks as compiled by countries in Central America, in particular Guatemala, and on concepts that are defined in the international standards of the System of National Accounts. Identities between the variables are exactly satisfied by the estimates. The method uses ratios between the variables as Bayesian conditions, and introduces prior reliabilities of values of basic data and ratios as criteria to adjust these values in order to satisfy the conditions. The paper not only presents estimates and precisions, but also discusses alternative conditions and reliabilities, in order to test the impact of framework assumptions and carry out sensitivity analyses. These tests involve, among others, the impact on Bayesian estimates of limited annual availability of data, of very low reliabilities (close to non-availability) of price indices, of limited availability of important administrative and survey data, and also the impact of aggregation of the basic data. We introduce the concept of `tentative' estimates that are close to conventional national accounts estimates, in order to establish a close link between the Bayesian estimation approach and conventional national accounting.
Original languageEnglish
Place of PublicationTilburg
Publication statusPublished - 2011

Publication series

NameCentER Discussion Paper


  • Macro accounts
  • system of national accounts
  • data frameworks
  • ratios
  • reliability
  • Bayesian estimation
  • sensitivity analysis
  • aggregation


Dive into the research topics of 'Bayesian Integration of Large Scale SNA Data Frameworks with an Application to Guatemala'. Together they form a unique fingerprint.

Cite this