macro integration
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Eurostat
Macro integration
Presented by
Piet Verbiest
Statistics Netherlands
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.
Macro integration
National accounts an example
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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Labour accounts
National accounts in the Netherlands
Supply and use tables
Sector accounts
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
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What we want:
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GDP production method 930 - 460 = 470GDP expenditure method 350 + 90 + 300 - 270 = 470
P M IC C I EThe Netherlands ltd 930 + 270 = 460 + 350 + 90 + 300
What we get:
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P M IC C I EThe Netherlands ltd 930 + 245 ≠ 450 + 350 + 90 + 275
GDP production method 930 - 450 = 480GDP expenditure method 350 + 90 + 275 - 245 = 470
P M IC C I EThe Netherlands ltd 930 + 245 ≠ 450 + 350 + 90 + 275
Whisky 5Other 930 + 240 ≠ 450 + 350 + 90 + 275
Macro integration / balancing
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GDP production method 930 - 450 = 480GDP expenditure method 350 + 90 + 275 - 245 = 470
5355
475355
Macro integration / balancing
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P M IC C I EThe Netherlands ltd 930 + 245 ≠ 450 + 355 + 90 + 275
Whisky 5 5Crude oil 40 20
Other 930 + 200 430 + 350 + 90 + 275
GDP production method 930 - 450 = 480GDP expenditure method 355 + 90 + 275 - 245 = 475
Industry dataRefineries
Production Fuel 30
Intermediate consumptionCrude oil 20other 5
Value added 5
20
225
495225
Macro integration / balancing
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GDP production method 930 - 450 = 480GDP expenditure method 355 + 90 + 275 - 225 = 495
P M IC C I EThe Netherlands ltd 930 + 225 ≠ 450 + 355 + 90 + 275
Whisky 5 5Crude oil 20 20Tablets 60 30 10
Components 50Other 870 + 200 ≠ 380 + 320 + 80 + 275
Industry dataComputer industry
ProductionTablets 60
Intermediate consumptionComponents 50other 5
Value added 5
20
295 275
295
50
465
275
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SUPPLY USEOutput of
industries Impo
rt
Tota
l
Input ofindustries
Cons
Expo
rt
Inve
st.
Tota
l
Com
mod
ities
YTotal P M IC+Y C E I
Value added
=P IC+ Y = GDP
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SUPPLY USE
Output of
industries Impo
rt
Tota
l
Input ofindustries
Cons
.
Expo
rt
Inve
st.
tota
l
Com
mod
ities
Y
Total P M IC+Y E C I
P - IC = Y = GDP P–IC = Y =C+I+E-M
P M IC C IE+ + ++=
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Commodities: 500 Industries: 150 Final expenditure: 20 Simultaneous: cup and cop
Domestic production Imports Total Final Totalsupply expenditure use
basic prices cif
Valuation
Taxes/
margins
subsidieson products
Value addedTotal output
Intermediate consumption
purchasers prices excl. VAT
Use tableSupply table
Total output
Non deductable VAT
trade andtransport
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Eurostat
Macro integration
Presented by• Jacco Daalmans
• j.daalmans@cbs.nl
Mathematical models
2+9=10
5=7
15/2=722=17
1=0
3+7=106=6
22=17+5
Mathematical Models
12+3+10=25
Mathematical models• Can be automated
• Reproducible results
• Flexible
• Large scale applications
BUT: Small discrepancies, without known cause
Example 1: WhiskyImports = Consumption
Given:
Imports = 5, Consumption=0
Model outcome could be: Imports= 2.5 Consumption = 2.5
NOT DESIRABLE!
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
Example 2: Remaining discrepancies
Production (P) = 930 928Imports (M) = 275 272Interm. Cons. (IC)= 450 455Cons. Invest. Export (CIE)= 740 745
P+ M = IC + CIE 1205 ≠ 1190 1200=1200P – IC = CIE – M 480 ≠ 465 473=473
Different models• RAS/IPF/RAKING
- easy, numerical technique - for a specific problem• STONE - broad scope of applicability
- mathematical optimization• DENTON (benchmarking)
- Time component (quarterly and annual data)
STONE’s Method• Broad applicability
• Achieves consistency by solving a minimum adjustment problem
STONE’s MethodSearches 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.
STONE’s MethodLinear constraints, like:
• Total is the sum of components: Manufacturing = Food + Textiles + Clothing;• Commodity balances; Total use = Total supply;• Definitions: Value added = Output – Intermediate consumption
ExtensionsInequality constraints:
Total Use ≥ 0Soft constraints:
Stocks of perishables goods ≈ 0Ratio constraints:
Value added Tax / Supply = 0.21
Refineries: use of crude oil / output ≈ 0.7
A man with a watch
knows what time it is
A man with two watches
is never sure
(Segal’s Law)
Reliability weightsImportant instrument to steer the results.
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
Example 2: Remaining discrepancies
Production (P) = 930 928 930Imports (M) = 275 272 270 Interm. Cons. (IC)= 450 455 450Cons. Invest. Export (CIE)= 740 745 750
P+ M = IC + CIE 1200=1200 1200=1200P – IC = CIE – M 473=473 480= 480
green = p and IC more reliable
Conclusions
Mathematical methods powerful instrument
Elaborate modelling constructions possible
But should be used properly!
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