Marcel Pommée National Accounts Department Statistics Netherlands (CBS)

1. Semi-automatic integration in Dutch Supply andUseTables – with special referenceto time-series Marcel Pommée National Accounts Department Statistics Netherlands (CBS)

2. Outline SUTs, characteristics Reasonsforautomation Automation with ‘machines’ Balancing machine Quarterly machine Time-series machine Time-series project Summary

3. SUTs, characteristics Annually, quarterly (t+45 and t+30 ?) Detail: industries (120), commodities (630), expenditurecategories (50) Focus on year on year changes, no seasonaladjustment Simultaneousbalancing of currentand constant prices Data gapsfilledwithassumptionsandextrapolations Manual balancing

4. Reasonsforautomation Efficiency gains: budget reductions up to 34% expected in 2018 Part of redesign of chain of economicstatistics: More structuredprocess Top-down approach Focus on major problems Quality Visibilityvariousadjustment steps (transparancy) Consistency over time Reproduce results (consistency) First gdpestimatesquicklyavailable(analysis)

5. Automation with ‘machines’ Quadraticoptimization model Minimizing the adjustmentneededto the growthrates of quarterly series T-1 andunbalanced data in currentand constant pricesavailable Only semi-automatic integration Major problemstackledmanually Small problemsresolvedthroughautomation Machines for different purposes: Balancing machine: balancing single SUT Quarterly machine: rebasingyearsandaligningquarters Time-series machine: rebasing time-series

6. Balancing machine Balancingsingle SUT: major problemssolvedmanually Hard and soft constraints Suppy is equaltouseby commodity Preserveprice indices by commodity Preserve i/o-ratiosby branches of industry Computetradeand transport margins Computetaxesand subsidies on products Upperandlowerboundsforindividual variables Fixation of variables Weighting of variables based on quality of datasource Specific relations (import and re-exports, building materialsandconstruction)

7. Quarterly machine Compilationcycle: final (F), preliminary (P), verypreliminary (V) Quarterlymachine Input: F-yearand 12 quarters of previouscycle Output: rebasedP- and V- yearand 12 alignedquarters Updating of P- and V-year Selectedinformation added Withbalancing machine

8. Time-series machine Time-series (1990-2009) based on benchmark revision 2010 Reconstruction of complete SU and IO tables Earlier Yearbyyearcompilation=> very time-consuming Difficulttopreservepriceand volume indices of original series Time-series machine New levels givenby benchmark yearandreferenceyears Reference years, e.g. 1987, 1995, 2001 (previousrevisionyears) Preservation of priceand volume indices of original series Iterativeprocess, manual intervention Result: fully consistent time-series in currentand constant prices

9. Time-series project (1) Year 2010 ESA 2010 conceptual revision and benchmark (statistical) revision Revised GDP 7,6% higher (concepts 3% and benchmark 4,6%) Covers Fully consistent ANA, QNA, ASA, QSA, LA, SUTs, IO-tables, and regional accounts Planning Benchmark year 2010 publication 6th March 2014 2001-2009, up to 2013 publication 20th June 2014 1995-2000 publication 24th September 2014 Extremely tight schedule

10. Time-series project (2) Time-series 2001-2009: series of problems Start-up problems time series machine (coding errors, retrieving data, capacity limitations, processing time) Takes a lot of time to specify constraints (fixation of variables, notably government data, weighting of variables, notably prices) Many interdependencies with other NA-modules (LA, ASA, government data and financial institutions, fisim) Time-series machine had to make quite large adjustments in the original series due to substantial level shifts in revision year

11. Time-series project (3) Time-series 2001-2009: consequences Difficult to understand what the machine is doing (black box) Planning deadlines were not met Hardly any documentation Data results machine less than optimal: extensive manual interventions => publication is on provisional basis Highly motivated team but tension and frustration due to adversities

12. Time-series project (4) Time series 1995-2000: gaining experience Compilation process split into parts with: Firstly a basic run without some constraints (no commodity balancing) => less adjustments by machine Secondly manual intervention to solve major problems (imbalances) Final run to solve minor inconsistencies and restore all constraints Results were much better Better understanding of the machine output Less or almost no manual intervention needed

13. Time-series project (5) Time series 1995-2000: some lessons learned Takes a lot of time to smoothly run the time-series machine Manual adjustments are mostly complex as they often affect large parts or the whole time-series Due to all interdependencies it is important to stick to the planning Complexities warrant an experienced team of compilers

14. Summary Almost 3 yearsexperiencewith ‘machines’ Pros Efficiency gains: in terms of lessfte’s More robuststatisticalprocess Improvedquality Cons Investment todevelopandtobuild Programming errors More complex: takes time tounderstandwhat the machine is doing