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SICENTER Ljubljana, Slovenia Time Distance – New Generic Approach for Analysis and Presentation of Time Related Data Professor Pavle Sicherl SICENTER and.

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Presentation on theme: "SICENTER Ljubljana, Slovenia Time Distance – New Generic Approach for Analysis and Presentation of Time Related Data Professor Pavle Sicherl SICENTER and."— Presentation transcript:

1 SICENTER Ljubljana, Slovenia Time Distance – New Generic Approach for Analysis and Presentation of Time Related Data Professor Pavle Sicherl SICENTER and University of Ljubljana Copyright © P. Sicherl All rights reserved Presented at the Time Distance Analysis conference at the George Washington University, October 25, 2005, Washington D.C.

2 TIME DISTANCE: Concept and novel generic statistical measure

3 A Comparison of European and US Economies Based on Time Distances The fact that comparisons should be made in two dimensions has been verified by the world- wide media interest in my analysis for the EUROCHAMBRES Spring Business Forum. The static ratio of 1.41 does not catch much attention, while the time gap of about two decades obviously produced a different perception of reality. The same will be true for comparing within the EU. Source: P. Sicherl, A Comparison of European and US Economies Based on Time Distances, EUROCHAMBRES, Brussels, March 2005

4 A NEW VIEW IN TIME SERIES ANALYSIS II. a. CONCEPT OF MULTIDIMENSIONAL COMPARISON AND EVALUATION b. PRESENTATION c. VISUALIZATION d. SEMANTICS: POLICY, MANAGEMENT PERCEPTION OF A SITUATION III. STOHASTIC MODELS WITH S-TIME-DISTANCE -e.g. criterion for evaluating forecasting models (Granger and Jeon, 2003) IV. DECISION MAKING MODELS - extension of decision making models FURTHER APPLICATIONS I. DESCRIPTIVE STATISTICAL MEASURE

5 A new view of the information using levels of the variable as identifiers and time as the focus of comparison and numeraire

6 The resulting time matrix provides new information from which new generic measures can be derived. Two operators applied to this time matrix lead to the derivation of two novel statistical measures, expressed in standardized units of time.

7 Source: P. Sicherl, Time Distance: A Missing Link in Comparative Analysis, 28th General Conference of the International Association for Research in Income and Wealth, Cork, Ireland, August

8 METHODOLOGY: a broader perception, policy and welfare

9 The perception whether the gender difference in life expectancy in the EU15 is large or small depends on the measure used: static percentage difference is only 8 percent, while S-time-distance amounts to 29 years. For a more realistic conclusion all measures should be presented simultaneously. Source: P. Sicherl, Time Distance As A New Additional Way To Measure And Assess The Overall Position Among And Within Countries, Discussion Paper, SICENTER, Ljubljana, 2004,

10 Importance for development strategy: the relations between growth, efficiency and inequality are different when based on a dynamic concept of overall degree of disparity Static relative measure and time distance lead to different conclusion: higher 4% growth example ratio=1.5, S=10 years; lower 1% growth example ratio=1.5, S=40 years. Per capita income (log scale) Higher growth rates lead to smaller time distances, and thus have an important effect on the overall degree of disparity. This is based on both static disparity and time distance, as both matter. Static measures alone are inadequate. Source: P. Sicherl, Time Distance: A Missing Link in Comparative Analysis, 28th General Conference of the International Association for Research in Income and Wealth, Cork, Ireland, August

11 Static measure and time distance show two very different messages about importance of different components Percentage differences between US and EU15 for employment rate, annual hours worked and productivity per hour are very similar. It seems as if the difficulty of catching up would be similar in the analysed components. S-time-distances are very different, for productivity per hour only 5 years, while for employment rate and annual hours worked are about a quarter of a century. Policy analysis should expect different difficulties of catching up in these fields.

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13 ANALYTICAL AND PRESENTATION TOOL

14 EMPIRICAL APPLICATIONS

15 UNITS OF COMPARISON MACRO, MEZZO, MICRO COUNTRIES REGIONS SECTORS CITIES SOCIO-ECONOMIC GROUPS COMPANIES HOUSEHOLDS PROJECTS INDIVIDUALS “Time distance is a generic concept. That means that, as it has been the case e.g. with spreadsheet, one cannot in advance specify all the uses to which a generic framework can be put by imaginative users in numerous fields.” J. Backhouse, Information Science Dpt., London School of Economics

16 FIELDS OF CONCERN ECONOMICS BUSINESS STATISTICS AND ECONOMETRICS SOCIAL SCIENCES TECHNOLOGIES AND SCIENCE STUDIES POLITICAL SCIENCE INEQUALITY COMPARATIVE POSITIONS GAP ANALYSIS NEW INSIGHTS FROM EXISTING DATA DUE TO AN ADDED DIMENSION OF ANALYSIS

17 PARADIGMATIC EXAMPLES TO BE USED IN PRESENTATION USA EU15 COMPARISON (EUROCHAMBRES STUDY) USA vs. JAPAN NEW MEMBER STATES AND EU15 AVERAGE ONE INDICATOR MANY COUNTRIES MANY INDICATORS TWO COUNTRIES 29 OECD COUNTRIES AND 3 INDICATORS: A NOVEL TYPOLOGY OF INDICATORS CONVERGENCE IN TWO DIMENSIONS GOODNESS-OF-FIT AND MONITORING IN TWO DIMENSIONS INTERNET USERS AND DIGITAL DIVIDE

18 TYPES OF ANALYSIS COMPARISON BETWEEN UNITS, BENCHMARKING COMPARISON ACROSS DIFFERENT TOPICS ACTUAL VS. ESTIMATED, FORECAST, PLAN, BUDGET, TARGET VALUES, MONITORING, MODELS, REGRESSIONS ANALYZING A SINGLE TIME SERIES

19 An example how analytical statistical tables can present time dimension in a new way Time matrix: time when a given indicator level was attained (penetration rates for PC per 100 inh.) Source: P. Sicherl, A New Perspective on Digital Divide, Time-distance Analysis: Method and Applications, eWISDOM, Journal for Comparative Research, 2a/2004

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21 COMPARING MANY COUNTRIES FOR A GIVEN INDICATOR FOR A YEAR GDP per capita (ppp): time distances for 2003 from EU15 average Source: P.Sicherl, ibid.

22 Time distance for EU23 countries from EU15 average for mobile phones per capita December 2003 Source: P.Sicherl, ibid.

23 Comparisons over many indicators can show characteristic profiles across countries, regions, socio-economic groups, firms, etc. Source: Interview with P.Sicherl - Semanario Economico, Lisbon, March 18, 2005

24 The time lag behind the USA over time has been the highest for employment rate and R&D per capita (25 and 23 years) Source: P. Sicherl, A Comparison of European and US Economies Based on Time Distances, EUROCHAMBRES, Brussels, March 2005.

25 Some hypothetical calculations: year in which EU15 would equalize with the USA for various assumptions Source: P. Sicherl, A Comparison of European and US Economies Based on Time Distances, EUROCHAMBRES, Brussels, March 2005.

26 Source: P. Sicherl, Time Distance As A New Additional Way To Measure And Assess The Overall Position Among And Within Countries, Discussion Paper, SICENTER, Ljubljana, 2004,

27 USA and Japan: very different conclusions about the magnitude of the gaps in the two dimensions

28 Source: P.Sicherl, ibid.

29 Very different conclusions about the magnitude of the gaps in the two dimensions among different indicators are also observed in comparing 29 OECD countries (see the graph) Development and welfare are multidimensional and long-term phenomena. Comparative analysis in attributes has to deal with these characteristics in a meaningful and consistent way. The two-dimensional approach shows a novel typology of indicators: type-I processes with long distances in time and short absolute or relative distances (e.g. life expectancy) type-II processes with short time distances and long distances in absolute or relative values (e.g. Internet users per capita) type-III processes with intermediate values of time distances and of absolute or relative distances (e.g. GDP per capita) An important question from the point of view of policy is to what extent are the wide differences in growth rates inherent in the nature of some particular attribute and to what extent can they quickly be changed by appropriate policy measures in accord with social objectives.

30 Growth rate effects and a broader concept of convergence Source: P.Sicherl, ibid.

31 Time S-time-distance adds a second dimension to comparing actual value with estimated value, forecast, budget, plan, target, etc. and to evaluating goodness-of-fit in regressions, models, forecasting and monitoring Variable X e1e1 S1S1 e2e2 e3e3 e4e4 e5e5 S2S2 S3S3 S4S4 S5S5 The generic idea for many other applications of S-time-distance

32 Source: P.Sicherl, Time Distance – Another Measure of Achieving Lisbon Targets, European Regional Economic Forum EREF-2005 Nova Gorica, Slovenia, October 2005

33 Source: P.Sicherl, ibid.

34 Monitoring and goodness-of-fit test in two dimensions The importance of using S-time-distance as a second dimension for monitoring and benchmarking across indicators in many fields is self explanatory, and immediately operational. A more long term scientific assignment is to develop optimizing procedures in models based also on the time distance deviations. E.g. Nobel prize winner Granger and Jeon (1997, 2003) further elaborated S-time-distance for the use as a criterion for evaluating forecasting models of leading and lagging indicators.

35 Time distance measure is intuitively understood by policy makers, managers, media and general public and is comparable across different variables, fields of concern, and units of comparison. Source: P.Sicherl, A New Generic Statistical Measure in Dynamic Gap Analysis, The European E-Business Report, 2004 Edition, European Commission, Enterprise Directorate General, Luxembourg, 2004

36 S-time-distances for buying online for sectors compared with the average percentage of diffusion for all sectors Source: P. Sicherl, Pilot study: Sectoral time-distances in the adoption of e-commerce activity, in The European e-Business Market Watch, Special Report, A Guide to ICT Usage Indicators, European Commission, DG Enterprise & Industry, 2005,

37 BENEFITS OF USING TIME DISTANCE ANALYSIS

38 SUMMARY: Benefits of immediate operational uses of time distance 2.1 A new view in competitiveness issues, benchmarking, target setting and monitoring for economic, employment, social, R&D and environment indicators at the world, EU, country, regional, city, project, socio- economic groups, company, household and individual levels 2.2 A broader dynamic framework for interrelating Lisbon strategy issues of growth, efficiency, inequality and convergence 2.3 Enhanced semantics for policy analysis and public debate 2.4 Additional exploitation of databases and indicator systems 2.5 An excellent presentation and communication tool -among different levels of decision makers and interest groups -for describing of the situations, challenges and scenarios -for proactive discussion and presentation of policy alternatives to policy makers, media, the general public and mobilizing those participating in or being affected by the programs -for communicating the urgent need for change and reforms


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