OECD Statistics Working Papers
The OECD Statistics Working Paper Series - managed by the OECD Statistics and Data Directorate – is designed to make available in a timely fashion and to a wider readership selected studies prepared by staff in the Secretariat or by outside consultants working on OECD projects. The papers included are of a technical, methodological or statistical policy nature and relate to statistical work relevant to the organisation. The Working Papers are generally available only in their original language - English or French - with a summary in the other.
Joint Working Papers:
Testing the evidence, how good are public sector responsiveness measures and how to improve them? (with OECD Public Governance Directorate)
Measuring Well-being and Progress in Countries at Different Stages of Development: Towards a More Universal Conceptual Framework (with OECD Development Centre)
Measuring and Assessing Job Quality: The OECD Job Quality Framework (with OECD Directorate for Employment, Labour and Social Affairs)
Forecasting GDP during and after the Great Recession: A contest between small-scale bridge and large-scale dynamic factor models (with OECD Economics Directorate)
Decoupling of wages from productivity: Macro-level facts (with OECD Economics Directorate)
Which policies increase value for money in health care? (with OECD Directorate for Employment, Labour and Social Affairs)
Compiling mineral and energy resource accounts according to the System of Environmental-Economic Accounting (SEEA) 2012 (with OECD Environment Directorate)
- ISSN: 18152031 (online)
- https://doi.org/10.1787/18152031
Nowcasting trade in value added indicators
Trade in value added (TiVA) indicators are increasingly used to monitor countries’ integration into global supply chains. However, they are published with a significant lag - often two or three years - which reduces their relevance for monitoring recent economic developments. This paper aims to provide more timely insights into the international fragmentation of production by exploring new ways of nowcasting five TiVA indicators for the years 2021 and 2022 covering a panel of 41 economies at the economy-wide level and for 24 industry sectors. The analysis relies on a range of models, including Gradient boosted trees (GBM), and other machine-learning techniques, in a panel setting, uses a wide range of explanatory variables capturing domestic business cycles and global economic developments and corrects for publication lags to produce nowcasts in quasi-real time conditions. Resulting nowcasting algorithms significantly improve compared to the benchmark model and exhibit relatively low prediction errors at a one- and two-year horizon, although model performance varies across countries and sectors.
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