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International Monetary Fund. Statistics Dept.
New data sources are now available in many countries to supplement direct collection of prices for the consumer price index (CPI). This technical assistance mission supported the National Bureau of Statistics of the Republic of Moldova with incorporating scanner data from the State Tax Service of the Republic of Moldova electronic sales monitoring system into the CPI.
Tsendsuren Batsuuri, Shan He, Ruofei Hu, Jonathan Leslie, and Flora Lutz
This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model robustness with regard to different feature sets and time periods. ML models consistently outperform traditional methods in out-of-sample prediction of new IMF-supported arrangements with key predictors that align well with the literature and show consensus across different algorithms. The analysis underscores the importance of incorporating a variety of external, fiscal, real, and financial features as well as institutional factors like membership in regional financing arrangements. The findings also highlight the varying influence of data processing choices such as feature selection, sampling techniques, and missing data imputation on the performance of different ML models and therefore indicate the usefulness of a flexible, algorithm-tailored approach. Additionally, the results reveal that models that are most effective in near and medium-term predictions may tend to underperform over the long term, thus illustrating the need for regular updates or more stable – albeit potentially near-term suboptimal – models when frequent updates are impractical.
International Monetary Fund. Statistics Dept.
This Technical Assistance report on Botswana focuses on details of National Accounts mission. The draft Census of Economic Establishments (CEE) questionnaires finalized during the previous mission in March 2023, have been tested. Some improvements were identified and have been incorporated into the CEE questionnaires. The mission and team commenced development of the data collection and data processing manuals. The deflator issues raised by the IMF country team were discussed. The Statistics Botswana (SB) indicated they had resolved these issues after the Ministry of Finance raised the same concern. SB are planning to use the tax list to identify the sample frame for this rebase. SB need to commence discussions with Botswana Unified Revenue Service to determine the sample frame as soon as possible. The sample design will have an impact on the data processing system. The mission reminded the team that they should be consulting now with the Population and Housing Census data processing team to obtain the data required for the rebase.
International Monetary Fund. Statistics Dept.
This paper presents technical assistance report on National Accounts Statistics mission in Botswana. The mission reviewed and suggested updates of two strategic documents pertaining to the rebase with the national accounts team. The first document broadly outlined the main objectives of a rebase and the key tasks. The second document concentrated on the more detailed implementation strategy. The timeline for the rebasing exercise was reviewed and agreed upon. The data input and processing system development is expected to be undertaken during 2023/24, commencing in May 2023. Therefore, the strategic documents, questionnaire design and sample frame need to be close to final by the end of April 2023. The national accounts team will need to start developing the information technology (IT) data processing system specifications so that in May 2023 they will have something to provide the IT consultant. It is recommended that Statistics Bostwana ensure that all of the required data is collected only once using the Agriculture Census form. Consequently, the national accounts team need to be involved in the development of the Agriculture Census form.
International Monetary Fund. Statistics Dept.
This technical assistance report on Philippines provides details about the Property Price Index mission. The authorities intend to improve the methods used for the Residential Real Estate Price Index and publish the Commercial Property Price Index (land only) for the first time. Some further improvements to the methods should be implemented, including amending the level at which the weights are applied in the aggregation process. The new methodology was evaluated to ensure that it adheres to international compilation standards and best practices. The authorities are committed to increasing the coverage of the statistics to include both cash purchases and other forms of non-bank lending. The existing data source used for the property price statistics are quarterly reports from the commercial banks. The report recommends to increase the capacity of the team with hedonic regression methods through attendance at a training course and/or planning further technical assistance.
International Monetary Fund. Statistics Dept.
This technical assistance (TA) mission on Government Finance Statistics was conducted during April 19– May 6, 2022. The main purpose of the mission was to review the progress made by the authorities in implementing previous TA recommendations and provide further support to improve fiscal data compilation and dissemination in line with international standards set out in the Government Finance Statistics Manual 2014.
International Monetary Fund. Statistics Dept.
This technical assistance mission collaborated with the National Institute of Statistics and Informatics in Peru to incorporate big data methods into compilation of the consumer price index (CPI). This includes both prices ingested from the websites of large retailers and data recorded through in-store checkout scanners.
International Monetary Fund. Statistics Dept.
The mission to Armenia took place between September 27–October 8, 2021 to assist the authorities to improve their Government Finance Statistics (GFS) compilation practices. The technical assistance (TA) mission was conducted by Ms. Ivana Jablonská and Mr. David Bailey at the request of the Ministry of Finance (MOF) and with the support of the IMF´s Middle East and Central Asia Department (MCD). The main objectives of the mission were to assist the authorities in finalizing a comprehensive sectorized list of all public sector units —known as, the public sector institutional table (PSIT) — and in compiling annual general government GFS data for 2020.