This space is dedicated to the discussion of methodological questions concerning short-term business statistics (STS ) in the European Statistical System.
MEETS Conference 2014
During the last period 2012 -2014 an MBGA was set up with a CBS as coordinator ans INE PT as new partner in place of Statistcs Estonia.
The EuroGroups Register (EGR ) is the European statistical register on multinational enterprise groups.
The main objectives for this project are the identification of best practices and the development of common methodology and ESS guidelines supporting the production of business statistics aiming at reducing respondent burden and fostering efficiency and integration of processes.
Summary results of action period 2010-2012
Summary results of action period 2009-2010
The main objectives of this project are to explore the possibilities of the use of admin data for business statistics, to make best practices in this area available to the NSIs, and to prepare recommendations on the efficient ways of producing business statistics by using data that are already available in the economy.
Presentation of the ESSnet EseG project:
Main objectives: A. To define the feasibility and the scope of ‘profiling’ large and complex MNEs;
B. The development of a common conceptual framework, methodology , rules and standards for ‘profiling’;
C. The development of process descriptions, tools, operational guidelines and quality assurance of profiling
[lexicon]The overall objective of the ESSnet is to strengthen ESS
The main objectives for this project are the identification of best practices and the development of common methodology and ESS
Summary results of action period 2008 - 2009
These pages constitute the Handbook on Methodology of Modern Business Statistics; it contains contributions from several European national statistical institutes. The handbook covers all statistical business process steps.
Administrative data are used more and more in official statistics as a replacement for survey data.
Guarnere U., Variale R. Estimation from contaminated multi-source data based on latent class models. Statistical Journal of the IAOS , vol. Preprint, no. Preprint, pp. 1-8, 2015
Estimation of bias and variance of the statistic of interest.