FAME 2 nd World Forum on Statistics, Knowledge and Policy Istanbul 2007 Measuring and Fostering the Progress of Society

2 A database designed for economic time series analysis SunGards Forecasting Modelling Analytical Environment (FAME) has long been viewed as a market leader in providing solutions to the Public Sector for time series storage and manipulation. FAME provides unparalleled database management facilities for storing time series data, The analytical tools give users the power as well as the flexibility required to improve the handling of many of the tasks of Central Banks and Statistical agencies. Six of the worlds 10 largest central banks and eleven of the worlds 20 largest commercial banks rely on FAME for the management of mission-critical time series data. FAME for the Public Sector

3 SunGard Data Management Solutions A Database designed for economic time series analysis which provides a number of unique features that are essential to Public Sector users: Efficient storage and retrieval of time series for unparalleled speed Data types specifically designed for storing time series Objects are stored separately from each other and accessed through their unique names, enabling models based on object name but with no relational overhead Naming conventions that accommodate the GESMES structure Only the raw data needs to be stored; Time Scale conversion is done ad- hoc User-defined attributes allows auxiliary data to be stored with the time series itself Client/server architecture can be combined with local databases Flexible graphing and reporting package Dynamically evaluated formulas e.g. building a national accounts model that automatically aggregates different economic levels FAME for the Public Sector

4 SunGard Data Management Solutions The FAME analytical engine offers a wide range of features including: Powerful manipulation and analysis of time series data means there are no constraints on the size of your models or the depth of your history 800 pre-defined analytical functions minimizes the amount of coding needed which means quick deployment and easy ad-hoc analysis Unique flexibility in time scale conversion without any coding Analytical routines can be saved and shared internally as well as with users in other public sector organization FAME is used primarily in 5 different areas of Public Sector institutions: Economic Research - Fast and easy ad-hoc econometric analysis Statistics Agencies – Dynamic models for calculating and validating aggregated measures of economic activity based on low-level raw data Monetary Policy/Monetary Analysis – Scenario analysis Banking Supervision – Simple dynamic aggregation of data Ministry of Finance – Fast ad-hoc analysis of data at different frequencies FAME for the Public Sector

5 SunGard Data Management Solutions FAME is heavily used by two directorates at the OECD: Economics Directorate Statistics Directorate FAME and the OECD

6 SunGard Data Management Solutions Analytical Database (ADB) Management System The ADB Management System is the Economic Departments ETL platform for managing macro economic data. The application, build using VBA and the FAME/OLE technology, uses Excel as a front-end for statisticians to define the data sources and management for time series from Member Countries. These user- friendly instructions are translated into FAME 4GL and extensive metadata are generated on the fly. Forecast Entry System (FE) Forecast Entry is a multi-country, multi-frequency accounting framework based on Excel and OLE technology. Country experts enter forecasts of input macro economic variables into Excel spreadsheets. Multiple, simultaneous write access to FAME FRDB databases ensures the computation in real time of all accounting identities and aggregates. FAME and the OECD – Economics Directorate

7 SunGard Data Management Solutions FAME Graphics The FAME 4GL is used to produce graphics for the Economic Studies and Economic Outlook publications. FAME/Populator The FAME/Populator is used widely in ECO, in order to bring FAME data into Excel for calculations or graphing. For example, the Populator is used to produce the tables in the Statistical Tables Annex to the Economic Outlook publication. OECD FAME Wizard The OECD FAME Wizard is an Excel-based GUI that uses the FAME/OLE technology. It allows users to browse and select data from FAME databases. Econometric work: regression, modelling Reports for internal documents FAME and the OECD – Economics Directorate

8 SunGard Data Management Solutions FAME is used in the maintenance of three principal SQL databases; MEI (Main Economic Indicators), QNA (Quarterly National Accounts), SNA (Annual National Accounts) Calculations: Calculations include: Seasonal adjustment Aggregations for composite indicators Chain linking The FAME-SQL communication is ensured by an interface written using the FAME C API Data Collection A FAME 4GL program is used to read GESMES/EDI files containing national accounts data submitted by Member Countries FAME and the OECD – Statistics Directorate

9 FAME: Object Oriented / Vector Based Database Highly tuned and optimized database structure Faster performance than relational databases Unique Time-Intelligent Architecture Cross sectional screening attributes Industrial strength, enterprise- wide data access FAMEs Core Technology consists of 2 components: 1.Database Engine 2.Analytical Engine FAME 4GL is the native querying language A full set of APIs (ODBC/JDBC, Java, C/C++, OLE server, web queries) gives any application fast and efficient read/write access to the data stored in FAME databases, as well as complete access to the FAME analytical functionality. This allows for easy and efficient integration between the FAME databases and statistical packages e.g. TROLL, TRAMO SEATS, EViews and SAS.

10 Tight, concise financial engineering language FAME Percent Change convert average freq business date 03jan05 to 28feb05 which not missing(yell.close) graph YELL.CLOSE, YELL.CLOSE[T-1], pct(YELL.close) SQL Percent Change SELECT DataPointDate, Close, (SELECT MAX(DataPointDate) FROM Yell R1 WHERE R1.InstrumentID = R.InstrumentID AND R1.DataPointDate < R.DataPointDate) AS PrevDate, (SELECT Close FROM Yell R1 WHERE R1.InstrumentID = R.InstrumentID AND R1.DataPointDate = (SELECT MAX(DataPointDate) FROM Yell R2 WHERE R1.InstrumentID = R1.InstrumentID AND R2.DataPointDate < R.DataPointDate)) AS PrevClose, (SELECT (((R.Close - R1.Close) / R1.Close) * 100) FROM Yell R1 WHERE R1.InstrumentID = R.InstrumentID AND R1.DataPointDate = (SELECT MAX(DataPointDate) FROM Yell R2 WHERE R1.InstrumentID = R1.InstrumentID AND R2.DataPointDate < R.DataPointDate)) AS CalcRead FROM YELL R WHERE DataPointDate > ( SELECT MIN(DataPointDate) FROM Yell R1 WHERE R1.InstrumentID = R.InstrumentID) AND DataPointDate BETWEEN DATE(' ') AND DATE(' ') ORDER BY InstrumentID, DataPointDate;

11 FAME Desktop