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Statistical research at the Joint Research Centre

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Title: Statistical research at the Joint Research Centre


1
Statistical research at the Joint Research
Centre Andrea Saltelli, andrea.saltelli_at_jrc.it
NTTS Conference, Brussels, February 2009
2
ERAWATCH Unit at JRC-IPTS (Seville)
Contact Pietro Moncada Paterno Castello (JRC
IPTS) pietro.moncada-paterno-castello_at_ec.europa.eu

3
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4
Industrial Research and Innovation at JRC -
Seville http//iri.jrc.ec.europa.eu/
  • The EU Industrial RD Investment Scoreboard
    analysis of 1000 EU and 1000 non-EU top investing
    companies in RD
  • The EU Survey of Business RD
  • Economic and policy analysis of corporate RD.

5
Some Results (1) Nature of the RD investment gap
Industrial Research and Innovation
  • EU's RD intensity deficit is largely explained
    by the different industrial structure (sectoral
    composition effect).

6
Industrial Research and Innovation
Some Results (2) Econometrics of RD firm
productivity
  • ?RD rises productivity much more in high-tech
    sectors than in low-tech ones

Source European Commission, JRC IPTS (2008)
Analysis of 2007 EU Industrial RD Investment
Scoreboard
7
Agrilife Unit at JRC-IPTS (Seville)
Contact Marc Müller (JRC IPTS) marc.mueller_at_ec.eu
ropa.eu
8
Building an Agro-Economic Modelling
PlatformDisaggregated Agricultural Social
Accounting Matrices for EU27 (AgroSAM)
9
  • Minimisation of Cross Entropy Measure (CE),
    subject to accounting constraints
  • AgroSAMs have to match EuroStat control totals
  • CE allows specifying confidence in data (higher
    confidence in cereal, oilseed, and dairy data,
    lower in fodder crops)
  • Contribution of EU27 AgroSAMs to GTAP database
    (2008)
  • Future Developments
  • Spatial coverage Regional SAMs (NUTS2)
  • Annual coverage Compilation of SAMs until
  • 2005 based on observations
  • 2010 based on projections

CAPRI Data
EuroStat Data
Unbalanced AgroSAM
Balancing procedure
10
Axel Tonini (JRC IPTS, Seville), Roel Jongeneel
(LEI, The Hague),2008, Modelling dairy supply for
Hungary and Poland by generalised maximum entropy
using prior information, European Review of
Agricultural Economics 35 (2) (2008) pp.
219-246.Müller, M. and I. Pérez Domínguez
(2008) Compilation of Social Accounting Matrices
with a Detailed Representation of the
Agricultural Sector (AgroSAM). Presented at the
11th Annual Conference on Global Economic
Analysis, Helsinki, Finland.  Müller, M., I.
Pérez Domínguez, and S.H. Gay (2009,
forthcoming) Construction of Social Accounting
Matrices for EU27 with a Disaggregated
Agricultural Sector, IPTS Technical
Documentation. 
11
  • Spatial Data Infrastructures Unit
  • of JRC-IES (Ispra)

Contact Jean Dusart jean.dusart_at_jrc.it
12
Spatial Data Infrastructures Unit (IES)
  • The Spatial Data Infrastructures Unit was
    established in 2006 as the JRC's response to new
    policy priorities. One such new priority regards
    the creation of a European Spatial Data
    Infrastructure, with a particular focus on the
    development and implementation of distributed
    information systems for environmental monitoring
    through in-situ and Earth observation techniques
    according to the INSPIRE Directive adopted in
    February 2007
  • Our mission is to coordinate the scientific and
    technical development of the Infrastructure for
    Spatial Information in Europe (INSPIRE), support
    its implementation within the Commission and the
    Member States, evaluate its social and economic
    impacts, and lead the research effort to develop
    the next generation of spatial data
    infrastructures

13
Harmonised multi-resolution geographical grid
(IES)
Context One method of storing spatial
information is by using geographical grids Equal
area grid suitable for generalising data,
statistical mapping, analytical work INSPIRE
Directive complemented by Implementing Rules
foresees the definition of a Pan-European Grid
based on a commonly agreed reference system
(ETRS89-LAEA) Grid defined as hierarchical (power
of 10) with associated coding system JRCs role
as technical coordinator of INSPIRE -
identify user requirements and develop
recommendations for grid specifications -
testing use case implementations
Applications European Population Grid
(JRC-EEA) Multi-scale Soil Information System
(Soil Action) Eco-pedological Map for the Alpine
Territory (Soil Action) SRTM DEM for Europe,
Corine Land Cover, LUCAS,
Grid 100 km
Grid 10 km
0,0
14
  • MARS Unit, JRC-IPSC (Ispra)

Contact Javier Gallego javier.gallego_at_jrc.it
15
LUCAS (Land Use/Cover Area frame Statistical
Survey)
  • 2001-2003

Relative efficiency
  • 2006
  • Role of JRC on LUCAS 2006
  • Optimising the sample
  • Efficiency assessment
  • Main task for 2009
  • Helping Eurostat to adapt 2006 sample to new
    priorities

16
Population density grid of the EU
  • Fine resolution (1ha)
  • Downloadable from EEA

Initial data population per commune
  • Downscaling
  • LUCAS
  • Reference data

17
MARS Crop Yield Forecasting System
  • In Europe
  • 35 countries covered
  • 11 crops monitored
  • 33 years of meteo and agrometeo data (daily data
    from 3000 stations)
  • 20 crops indicators are daily simulated by crop
    models
  • 21 years of low resolution satellite information

18
  • Econometrics and Applied Statistics Unit,
    JRC-IPSC (Ispra)

19
  • ECOTRIM (Riccardo)

20
  • QUEST III (Riccardo)

21
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22
Rickety Numbers International rankings of
higher education lack statistical robustness
Michaela Saisana, Beatrice D'Hombres, Andrea
Saltelli ?
UNE ÉTUDE QUI MET EN CAUSE LE CLASSEMENT DE
SHANGHAÏ
See www.lemonde.fr
Saturday 15 November 2008
http//www.lemonde.fr/archives/article/2008/11/14/
vers-un-classement-europeen-des-universites_111844
8_0.html
23
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24
Global sensitivity analysis
Courses every year Venice Brussels Ispra
2000
2004
2008
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