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Published byVirgil Fletcher Modified over 9 years ago
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Eurostat Macro integration
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Presented by Piet Verbiest Statistics Netherlands
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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.
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Macro integration National accounts an example
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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
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7 Labour accounts National accounts in the Netherlands Supply and use tables Sector accounts
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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
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What we want: 9
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What we get: 10
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Macro integration / balancing 11 5 355 475 355
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Macro integration / balancing 12 20 225 495225
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Macro integration / balancing 13 20 295275 295 50 465 275
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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
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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++++=
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16 Commodities: 500 Industries: 150 Final expenditure: 20 Simultaneous: cup and cop
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Eurostat Macro integration
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Presented by Jacco Daalmans j.daalmans@cbs.nl
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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
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Mathematical models Can be automated Reproducible results Flexible Large scale applications
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BUT: Small discrepancies, without known cause
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Example 1: Whisky Imports = Consumption Given: Imports = 5, Consumption=0 Model outcome could be: Imports= 2.5 Consumption = 2.5 NOT DESIRABLE!
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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
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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
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Different models
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STONE’s Method Broad applicability Achieves consistency by solving a minimum adjustment problem
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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.
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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
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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
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A man with a watch knows what time it is A man with two watches is never sure (Segal’s Law)
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Reliability weights Important instrument to steer the results.
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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
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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
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Conclusions Mathematical methods powerful instrument Elaborate modelling constructions possible But should be used properly!
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