3. Two time series can and should be compared in two dimensions: 1. static gap for a given point in time 2. gap in time for a given level of the variable
4. A new view of the information using levels of the variable as identifiers and time as the focus of comparison and numeraire
5. 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 .
6. 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 22-28 2004
10. 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-distance s 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 .
11. 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
12. Importance for European development paradigm: the relations between growth, efficiency and inequality in Lisbon strategy are different with 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= 4 0 years . Per capita income (l og 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.
13. Variable X e 1 S 1 e 2 e 3 e 4 e 5 S 2 S 3 S 4 S 5 The generic idea for many other applications of S-time-distance 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
14. S-time-distance: S (X t ) = t(X t ) – T(X t ) S (X t ) = actual time t – time on the line to target T for each actual value of the variable X t S (66.0 2006 ) = 2006 (66.0 2006 ) – 2003.9 (66.0 2006 ) = 2.1 years
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16. Lisbon 1 target of 70% employment rate in 2010 for all countries (deviations in the time dimension from the hypothetical path to target) S-time-distance in years: - actual ahead of path to target, + actual behind the path to target S-time-distance in years
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18. Implementation of national targets for the R&D share in GDP in 2005 (deviations in the time dimension from the hypothetical path to target) S-time-distance in years: - actual ahead of path to target, + actual behind the path to target
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23. Monitoring implementation of Lisbon 1 targets for 2010 for EU15 S-time-distance in years: - actual ahead of path to target, + actual behind the path to target S-time-distance in years (deviations in the time dimension from the hypothetical path to target)
24. Monitoring implementation of the Millennium Development Goals in the time dimension for selected indicators: CHINA, situation around 2004 S-time-distance in years: - actual ahead of path to target, + actual behind the path to target