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International Monetary Fund. Statistics Dept.
A technical Assistance (TA) Mission was conducted by CAPTAC-DR1 from May 14 to 18, 2018 with the objective of supporting the Central Reserve Bank of El Salvador (CBR) in its efforts to strengthen its national accounts statistics for decision making. The TA mission covered the following topics: compilation of an Input-Output Table (IOT) for 2014; as well as to follow up on the recommendations made in previous TA missions to disseminate Supply and Use Tables (SUT) for 2015 and thereafter, as part of the national accounts’ series with base year 2005. In addition, the mission provided training to the Department of National Accounts (DNA) team of the CBR in the methodological and conceptual aspects necessary for the analysis and application of the IOT as a statistical and analytical tool.
International Monetary Fund. Statistics Dept.

A technical Assistance (TA) Mission was conducted by CAPTAC-DR1 from May 14 to 18, 2018 with the objective of supporting the Central Reserve Bank of El Salvador (CBR) in its efforts to strengthen its national accounts statistics for decision making. The TA mission covered the following topics: compilation of an Input-Output Table (IOT) for 2014; as well as to follow up on the recommendations made in previous TA missions to disseminate Supply and Use Tables (SUT) for 2015 and thereafter, as part of the national accounts’ series with base year 2005. In addition, the mission provided training to the Department of National Accounts (DNA) team of the CBR in the methodological and conceptual aspects necessary for the analysis and application of the IOT as a statistical and analytical tool.

Emanuel Kopp, Lincoln Kaffenberger, and Christopher Wilson

Cyber Risk A. Cyber as an Operational Risk B. Guidelines VI. Measures to Strengthen Resilience to Cyber Risk A. Reducing Access Vulnerabilities while Boosting Resilience B. Lessening Information Asymmetries C. Designing Effective Policies D. Address Coordination Failures and Manage Systemic Cyber Risk References Boxes 1. Recent Cyber Attacks on the Financial Services Industry 2. Cyber Insurance 3. Approach to Critical Infrastructure Figures 1. Internet of Things: Devices Connected to the Internet (August 2014) 2. Sources of Threat by Type

Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero

business and administrative systems, social networks, and the internet of things. To structure the discussion, the paper presents a big data classification that is relevant for macroeconomic and financial statistics. What is the potential of big data? Big data can benefit macroeconomic and financial statistics and ultimately policymaking through at least three features: 1. By answering new questions and producing new indicators 2. By bridging time lags in the availability of official statistics and supporting the timelier forecasting of existing indicators 3

Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero

, in both operational and business intelligence systems—usually structured and stored in relational database systems. (Some sources belonging to this class may fall into the category of “Administrative data.”) 21. Data Produced by Public Agencies 2110. Medical records 22. Data Produced by Businesses 2210. Commercial transactions 2220. Banking/stock records 2230. E-commerce 2240. Credit cards 3. Internet of Things (machine-generated data) : This information is derived from the phenomenal growth in the number of sensors and machines used