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.
At the request of the Ministry of Economy and Finance (MEF) and in consultation with the Africa Department (AFR) of the International Monetary Fund (IMF), a remote Government Finance Statistics (GFS) mission from the Statistics Department (STA) took place in Madagascar from October 26 to November 13, 2020. The objective of this mission was to continue supporting the authorities in their project to adopt international GFS standards based on the methodology of the Government Finance Statistics Manual 2014 (GFSM 2014) and the Public Sector Debt Statistics Guide (PSDSG) and to improve GFS in general.
This Technical Assistance Report discusses the findings and recommendations of the IMF mission regarding compilation of Government Finance Statistics in Bosnia and Herzegovina as per the Government Finance Statistics Manual 2014 and the European System of National and Regional Accounts 2010. On the compilation of Excessive Deficit Procedure (EDP) tables, the mission assisted the Central Bank of Bosnia and Herzegovina with the completion of derivation tables for net lending / net borrowing of the budgetary governments of the Federation of Bosnia and Herzegovina and the Republic of Srpska. The mission also recommended implementing a coding system for statistical adjustments to the source data; applying the superdividend test; and further implementing derivation tables for the compilation of EDP tables.
Federico Diaz Kalan, Ms. Adina Popescu, and Julien Reynaud
There is evidence that fiscal rules, in particular well-designed rules, are associated with lower sovereign spreads. However, the impact of noncompliance with fiscal rules on spreads has not been examined in the literature. This paper estimates the effect of the Excessive Deficit Procedure (EDP) on sovereign spreads of European Union member states. Based on a sample including the 28 European Union countries over the period 1999 to 2016, sovereign spreads of countries placed under an EDP are found to be on average higher compared to countries that are not under an EDP. The interpretation of this result is not straight-forward as different channels may be at play, in particular those related with the credibility and the design of the EU fiscal framework. The specification accounts for typical macroeconomic, fiscal, and financial determinants of sovereign spreads, the System Generalized Method of Moments estimator is used to control for endogeneity, and results are robust to a range of checks on variables and estimators.
This Technical Assistance Report assesses the current government finance statistics compilation environment in Bosnia and Herzegovina (BiH), particularly the classification of institutional units and debt data compilation based on the Government Finance Statistics Manual and European System of National and Regional Accounts frameworks. It was found that a full list of public sector units in BiH does not currently exist. The Central Bank of Bosnia and Herzegovina (CBBH) is in a good starting position to compile the public sector debt data. The results of its work on financial balance sheets should enable the CBBH to produce general government debt data in a relatively short term.
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 analyzes the effectiveness of technical assistance provided by AFRITAC West (AFW) in the area of national accounts using the Fund's Technical Assistance Information Management System (TAIMS). The challenge has been to report on "ultimate outcomes" (i.e., the production and dissemination of national accounts statistics along best international practices) rather than on "inputs" (i.e., the number of national accounts missions fielded by AFW), as it has been the case to date. The paper concludes that the "ultimate outcome" of producing and disseminating robust national accounts is work in progress, with AFW's technical assistance efforts mainly focusing on source data assessments and methodological issues underpinning the compilation of national accounts. The pending challenge is to further support a more timely production and dissemination of national accounts data, as recommended in the Data ROSCs and by the IMF mission teams to AFW member countries.
This paper constructs a data set to document firms' expenditures on an identifiable list of intangible items and examines the implications of treating intangible spending as an acquisition of final (investment) goods on GDP growth for Canada. It finds that investment in intangible capital by 2002 is almost as large as the investment in physical capital. This result is in line with similar findings for the U.S. and the U.K. Furthermore, the growth in GDP and labor productivity may be underestimated by as much as 0.1 percentage point per year during this same period.
Default prior choices fixing Zellner's g are predominant in the Bayesian Model Averaging literature, but tend to concentrate posterior mass on a tiny set of models. The paper demonstrates this supermodel effect and proposes to address it by a hyper-g prior, whose data-dependent shrinkage adapts posterior model distributions to data quality. Analytically, existing work on the hyper-g-prior is complemented by posterior expressions essential to fully Bayesian analysis and to sound numerical implementation. A simulation experiment illustrates the implications for posterior inference. Furthermore, an application to determinants of economic growth identifies several covariates whose robustness differs considerably from previous results.