Bayesian estimation approach in frameworks, integration of - - PowerPoint PPT Presentation

 Bayesian estimation approach in frameworks initially designed for national accounting, by which compilation formalized and computerized, now also extended to satellite systems with economic, and physical, and other data  Frameworks = logical frameworks of (i) prior values of data; (ii) variables to be estimated; (iii) ratios + identities between variables; (iv) prior reliability coefficients of data and ratio values (σ/value), based on subjective assessment of data and ratio values available  Logical = forming a system that serves the purpose of predefined types of analysis: Variables selected & frameworks constructed to estimate posterior values of all variables and their posterior reliability coefficients. Variables and ratios necessary and relevant for desired analysis  Number of information items (data, ratio values, identities) may be much larger than number of variables to be estimated. Posterior reliabilities much more precise than prior

• nes, due to mutual confrontation of large number of information items in frameworks,

that include available data and establish conditions (identities, relations) which must be fulfilled  Posterior estimates made by minimizing the squared differences between prior and posterior values, under conditions of pre-established identities and ratios between variables, also taking into account the prior reliabilities of the values. Inverted values of variances (1/σ2) used as weights prior data values with higher variance adjusted more.

Bayesian estimation approach in frameworks, integration of compilation and analysis

Jan W. van Tongeren [jwvtongeren@gmail.com] & Ruud Picavet [ruud@picavet.com]

 Prior values of ratios used in compilation can also be used in their posterior format as analytical indicators  Thus: Through selection of relevant variables, identities and ratios for logical framework de facto integration of compilation and analysis, which is direct, effective, and efficient  Through formalization and computerization and mutual confrontation of more information items: improved accuracy and quicker estimates  Through quicker estimates, more over time updates possible, also with fewer data. Approach responds effectively to scarcity of data  System not necessarily linked to SNA. Much more flexible and comprehensive: e.g. environmental accounts; socio-economic poverty analysis; economic-demographic National Transfer Accounts. Responds to use of data in all kinds of analysis  Assignment of prior reliability coefficients based on subjective assessment

• f available data and ratio values, but explicitly, not implicitly as in

conventional national accounting  Capacity of accounting considerable expandable within limits set by software

Integration of compilation and analysis. Pros and cons of method

 Various applications: Central Banks of Guatemala & Dominican Republic; St. Vincent, Kurdistan. Comparable methods used by Statistics Netherlands  Quantitative assessment by comparing (for Guatemala) values and posterior estimates based on comprehensive (2005), and limited (2006) data sets

Assessment illustrated through applications

 Differences between Bayesian and NA estimates (Guatemala 2005) very minor, but all estimates improve in accuracy: posterior values of reliability coefficients of variation significantly reduced (e.g. GDP prior 3%, posterior 0.01%)  With fewer data available (Guatemala 2006) posterior estimates deviate more (e.g. in 2006, HH consumption posterior value deviates 2.7% and gross fixed capital formation - 5.4%)  Bayesian estimation within frameworks generates accurate posterior estimates , even in case of limited data. Estimates within frameworks more reliable, because identity and ratio relationships taken into account  Through formalization and computerization more accurate in less time, also with less data.  Frameworks defined once, with annual small updates. Framework largely independent

• f data availability at different points in time. Limited hardware and software needs.

Thus requires less training and therefore easier and cheaper to implement.  Design of framework is based on a mixture of requirements of analysis and availability

• f data. The more ratios and identities incorporated in framework, the more precise

posterior estimates + the richer posterior analysis  Frameworks optimal for link between national economic accounting and satellite accounting (= flexibility and desired analysis) and less data availability  Future developments of data (big data) and new types of analysis better supported

Assessment and conclusions