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Eurostat Macro integration. Presented by Piet Verbiest Statistics Netherlands.

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Presentation on theme: "Eurostat Macro integration. Presented by Piet Verbiest Statistics Netherlands."— Presentation transcript:

1 Eurostat Macro integration

2 Presented by Piet Verbiest Statistics Netherlands

3

4 Macro integration Reconciliation of inconsistent statistical data on a high level of aggregation Balancing is reconciling inconsistent statistical information from independent sources brought together in an ‘accounting’ framework consisting of well-defined variables, accounting identities on combinations of variables and other less strict relations between the sets of variables.

5 Macro integration National accounts an example

6 National accounts Comprehensive overview of all economic transactions in a country Quarterly and annual report of a country Key indicators  Gross domestic product (GDP): economic growth;  Gross national income  Consumption of households, investment, foreign trade  Government debt  Employment

7 7 Labour accounts National accounts in the Netherlands Supply and use tables Sector accounts

8 Supply and use tables Variables and basic identities identities (1) P + M = IC + C + I + E (2) Y = P - IC (3) Y = C + I + E - M (4) Y = W + OS/MI 8

9 What we want: 9

10 What we get: 10

11 Macro integration / balancing 11 5 355 475 355

12 Macro integration / balancing 12 20 225 495225

13 Macro integration / balancing 13 20 295275 295 50 465 275

14 14 SUPPLYUSE Output of industries Import Total Input of industries Cons Export Invest. Total Commodities Y Total PM IC+YCEI Value added = PIC+Y = GDP

15 15 SUPPLY USE Output of industries ImportTotal Input of industries Cons. Export Invest. total Commodities Y TotalPMIC+YECI P - IC = Y = GDP  P–IC = Y =C+I+E-M PMICCIE++++=

16 16  Commodities: 500  Industries: 150  Final expenditure: 20  Simultaneous: cup and cop

17 17

18 Eurostat Macro integration

19 Presented by Jacco Daalmans j.daalmans@cbs.nl

20 Mathematical models 2+9=10 5=7 15/2=7 22=17 1=0 3+7=10 6=6 22=17+5 Mathematical Models 12+3+10=25

21 Mathematical models Can be automated Reproducible results Flexible Large scale applications

22 BUT: Small discrepancies, without known cause

23 Example 1: Whisky Imports = Consumption Given: Imports = 5, Consumption=0 Model outcome could be: Imports= 2.5 Consumption = 2.5 NOT DESIRABLE!

24 Example 2: Remaining discrepancies Production (P) = 930 Imports (M) = 275 Interm. Cons. (IC)= 450 Cons. Invest. Export (CIE)= 740 P+ M = IC + CIE 1205 ≠ 1190 P – IC = CIE – M 480 ≠ 465

25 Example 2: Remaining discrepancies Production (P) = 930 928 Imports (M) = 275 272 Interm. Cons. (IC)= 450 455 Cons. Invest. Export (CIE)= 740 745 P+ M = IC + CIE 1205 ≠ 1190 1200=1200 P – IC = CIE – M 480 ≠ 465 473=473

26 Different models

27 STONE’s Method Broad applicability Achieves consistency by solving a minimum adjustment problem

28 STONE’s Method Searches for a result with minimum deviation from the input. Mathematical: Translation to a least squares optimization problem Consistency rules translate to constraints of the model.

29 STONE’s Method Linear constraints, like: Total is the sum of components: Manufacturing = Food + Textiles + Clothing; Commodity balances; Total use = Total supply; Definitions: Value added = Output – Intermediate consumption

30 Extensions Inequality constraints: Total Use ≥ 0 Soft constraints: Stocks of perishables goods ≈ 0 Ratio constraints: Value added Tax / Supply = 0.21 Refineries: use of crude oil / output ≈ 0.7

31 A man with a watch knows what time it is A man with two watches is never sure (Segal’s Law)

32 Reliability weights Important instrument to steer the results.

33 Example 2: Remaining discrepancies Production (P) = 930 928 Imports (M) = 275 272 Interm. Cons. (IC)= 450 455 Cons. Invest. Export (CIE)= 740 745 P+ M = IC + CIE 1200=1200 P – IC = CIE – M 473=473

34 Example 2: Remaining discrepancies Production (P) = 930 928 930 Imports (M) = 275 272 270 Interm. Cons. (IC)= 450 455 450 Cons. Invest. Export (CIE)= 740 745 750 P+ M = IC + CIE 1200=1200 1200=1200 P – IC = CIE – M 473=473 480= 480 green = p and IC more reliable

35 Conclusions Mathematical methods powerful instrument Elaborate modelling constructions possible But should be used properly!


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