International organizations collect data from national authorities to create multivariate cross-sectional time series for their analyses. As data from countries with not yet well-established statistical systems may be incomplete, the bridging of data gaps is a crucial challenge. This paper investigates data structures and missing data patterns in the cross-sectional time series framework, reviews missing value imputation techniques used for micro data in official statistics, and discusses their applicability to cross-sectional time series. It presents statistical methods and quality indicators that enable the (comparative) evaluation of imputation processes and completed datasets.
This paper suggests a way forward in the effort to measure statistical capacity building by combining features of two tools – the Project Management System, a logical framework methodology that the IMF Statistics Department uses to plan, monitor, and evaluate technical assistance projects, and the Data Quality Assessment Framework, a methodology for assessing data quality that brings together best practices and internationally accepted concepts and definitions in statistics