Upon completion of this program, participants will be able to:

• Explain the role of the central bank as a collector and producer of official economic statistics.
• Apply the principles of international statistical standards (e.g., SNA, BPM6) to data compilation.
• Evaluate data quality using standard statistical metrics (e.g., accuracy, reliability, timeliness).
• Design a robust **data governance** framework for ensuring data integrity and confidentiality.
• Describe the methodologies for compiling key monetary, financial, and external sector statistics.
• Manage the challenges of integrating Big Data, alternative data, and supervisory data into official statistics.
• Understand the legal and ethical requirements for data collection, confidentiality, and dissemination.
• Formulate strategies for improving statistical capacity and compliance with international guidelines (e.g., IMF SDDS).

• Central Bank Statistics and Data Analysts
• Economists and Researchers reliant on Official Data
• Regulatory Reporting and Data Governance Professionals
• IT and Data Architects supporting Statistical Production
• Government Statistical Agency Personnel
• Internal Audit and Compliance Professionals for Data Quality

Data quality assurance exercises, Group project on assessing a statistical report against BPM6, Workshops on monetary aggregate compilation, Technical deep dives into data governance principles, Discussions on Big Data integration challenges, Role-playing a data collection audit.

Unit 1: The Statistical Mandate and Standards

• The central bank's statutory mandate for collecting, compiling, and disseminating financial statistics.
• Overview of the **System of National Accounts (SNA)** and its relevance to central bank data.
• The importance of international harmonization (e.g., IMF, BIS) in statistical reporting.
• Principles of official statistics: impartiality, methodological soundness, and accessibility.

• Methodology for compiling Monetary Aggregates (M1, M2, M3).
• Compilation of balance sheet and interest rate statistics for the banking sector.
• The use of supervisory data (e.g., FINREP/COREP) for statistical purposes.
• Challenges in measuring financial innovation and new financial instruments.

Unit 2: External Sector and National Accounts Data

• The framework for the **Balance of Payments (BOP)** and the **International Investment Position (IIP)** (BPM6).
• Methodology for collecting and compiling data on direct investment, portfolio investment, and debt.
• Reconciliation and data sharing with national statistical agencies (NSAs) on trade and capital flows.
• Challenges in measuring capital flight and shadow economy activities.

• Central bank involvement in GDP compilation and interpretation.
• Methodology for compiling and analyzing price indices (CPI, PPI) for inflation analysis.
• Use of advanced sampling and survey techniques in data collection.
• Techniques for seasonal adjustment and managing data revisions.

Unit 3: Data Governance and Quality Assurance

• Dimensions of data quality (accuracy, reliability, consistency, timeliness, relevance).
• Developing a robust data validation and cleaning process.
• Techniques for managing missing data, outliers, and data reporting errors.
• The role of metadata and documentation in ensuring transparency.

• Establishing a formal **Data Governance** framework (roles, policies, standards).
• Legal authority for data collection and mandatory reporting requirements.
• Ensuring data confidentiality, privacy, and secure data storage.
• Compliance with international data dissemination standards (e.g., IMF SDDS/GDDS).

Unit 4: Big Data and Future Challenges

• The potential and challenges of using Big Data (e.g., web scraping, satellite imagery) for economic statistics.
• Techniques for linking and integrating administrative data with official statistics.
• Policy and ethical considerations for using non-traditional data sources.
• The role of Central Bank Digital Currency (CBDC) in generating new payment data.