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
The contents of this report constitute technical advice provided by the staff of the IMF to the authorities of Bangladesh in response to their request for technical assistance. The purpose of the mission was to assist the Bangladesh Bank (BB) in progressing on the compilation of a residential property price index. BB has plans to set up a new data collection system to improve the current existing data starting from July 2020. The ne