1. Professor Pavle Sicherl SICENTER and University of Ljubljana
Ljubljana, Slovenia
   
‘How long will it take to catch up?’
A new approach to translate gaps from time series statistics into time distances
Professor Pavle Sicherl
SICENTER and University of Ljubljana
Paper presented at the workshop organised by DG Enterprise and the e-Business Brussels, December 10, 2003
Copyright © P. Sicherl All rights reserved

4. A NEW VIEW IN TIME SERIES ANALYSIS
I. DESCRIPTIVE STATISTICAL MEASURE
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-DISTANCE
e.g. criterion for evaluating forecasting models
IV. DECISION MAKING MODELS
- extension of decision making models
APPLICATION
FURTHER

5. A Novel Generic Statistical Measure: Using Levels of the Variable as Identifiers and Time as the Focus of Comparison and Numeraire

6. Digital divide between the North America and Western Europe for Internet users per 1000 inhabitants: S-distance and S-step

8. Time matrix: time when a given indicator level was attained in each country
(an example how statistical tables can present time dimension in a new way)

12. Ex post and ex ante S-distance: distance in time (projected) at the level of EU15 average GDP per capita for 2000, scenario: growth rate 4% CEEC

14. Typology of indicators - time distance and static disparity present very different conclusions for indicators with different growth rates: female life expectancy (Type I - slow dynamics) and Internet host per capita (Type II - fast dynamics)

15. A1 E1
E1L
e=A1-E1
S1=t(A1)-t(E1L)
Time
Indicator X
The time distance concept (S-distance) adds the second dimension in comparing
actual value vs.estimated value, forecast, budget, plan, target, etc. and evaluating degree of dispersion in regressions, models, forecasting and monitoring e2 e4 e3 e5
e(t)5
e(t)3
e(t)2
e(t)4