News[2024]

This course introduces the SEEA Central Framework, the international statistical standards for measuring the interactions between the environment and the economy. It is comprised of 5 modules. The first module introduces the SEEA. Modules 2 discusses accounting for physical flows while module 3 discusses accounting for environmental assets. The fourth module contains details on environmental activity accounts, combined presentations and indicators. The last module contains an overview of subsystems, applications and extensions.

Statistical Business Registers (SBRs) are often described as the backbone of economic statistics, as they provide the core infrastructure to support the collection of economic data and the production of economic statistics. They provide a coherent set of units and classifications to collect and compile data across all domains, and a consistent set of rules to maintain this set of units over time. SBRs are considered as the backbone for producing economic statistics that meet the increasing demand for better integrated, coherent and comparable statistics across countries and statistical domains. Inclusive and exhaustive SBRs are becoming an increasingly more important element of the statistical infrastructure for maintaining the relevance, responsiveness and quality of economic statistics in order to measure the structure and dynamics of economic activity. 

This webinar will provide the participants with a clear understanding of the importance of measuring digital trade, and the conceptual framework for measuring digital trade whilst focusing on the fundamental concepts and statistical definitions. A variety of examples and practical applications will be shared by country representatives. The webinar will also emphasize the importance of digital trade statistics in evidence-based policy making. 

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Pacific Community (SPC) has started an initiative to explore and identify the unmet needs for statistics in the Pacific region and undertook the region-wise comprehensive needs assessment. This needs assessment work will contribute to the development of the regional Capacity Development Framework, which is aimed to identify the needs and the appropriate capacity development modalities at three levels: system, institutional/organisational, and individual - more information can be found in a working paper aimed to highlight main features of the framework. The webinar is aimed at streamlining the statistical capacity development in the Pacific by making informed decisions and identifying systemic areas to focus on in the coming years. 

The Statistics Department (STA) of the International Monetary Fund (IMF), is implementing the “Environment and Climate Change Statistics Capacity Development Program”, supported by the State Economic Cooperation (SECO). The program is oriented towards assisting beneficiary countries in the development and dissemination of indicators most relevant to their policy needs. Accordingly, technical assistance will focus on developing one or two indicators that reflect the most urgent data needs of the targeted countries; and encompass the use of internationally agreed methodology or testing methodology underdevelopment. The program will build capacity in the project countries to compile select indicators through the organization of workshops, trainings, and targeted hands-on technical assistance.

Macroeconomic accounts provide comprehensive and detailed records of the complex economic activities taking place within an economy, and of the interactions between different economic agents, and groups of agents, in markets or elsewhere. As such, harmonization and consistency of the accounting framework allow economic data to be compiled and presented in a format that supports economic analysis, and policy decision making. This webinar series will provide the participants with a clear understanding of the importance of macroeconomic statistics and its components to an economy, whilst focusing on the fundamentals of the macroeconomic and financial statistics. This webinar will also enable the participants to have a comprehensive appreciation of the statistical linkages within the macroeconomic statistics framework.

This 8-week course developed by SIAP in partnership with the Asian Development Bank (ADB) introduces machine learning as a tool for using either traditional (surveys, micro data, …) or non-traditional (big data) data sources to produce high quality predictions for Official Statistics or Sustainable Development Goals (SDGs) indicators. It provides an opportunity for participants to explore and manipulate the techniques of Machine Learning and their links with traditional statistical methods. The 6 modules (+1 module with recalls/prerequisites) aim at providing an overview of the current methods and applications of Machine Learning, through simplified theoretical concepts, pedagogical case studies and interactive resources. The course is not based, nor does it require, a particular software. However, reproducible examples on either simulated or real data are provided using the R/RStudio environment. Some Python procedures and packages are also provided.