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Time distance method in ‘2011 State of the Future’
Wednesday, 09 November 2011

Discussion of possible application to longer series of the past and of future prospects

Millennium Project published its yearly report ‘2011 State of the Future’ presenting the overview of the prospects of the world development. Professor Pavle Sicherl as a member of the Millennium Project South East Europe Node contributed in the Appendix State of the Future Index the section on Time Distance Method.

It contains history of the method, its description, how it is calculated, the strengths and weaknesses as well as frontiers of the method. Empirical examples contain analysis of the Human Development Index, life expectancy in the very human development group and compressed presentation of analysis and projections over 100 years (1950-2050) for elderly population for OECD countries.

The strength of the S-time-distance concept lies in the fact that it enables additional exploitation of data and an alternate means of visualization of time related databases and indicator systems. Level-time matrix provides (with some interpolation) a visual impression of both levels of the indicator and the number of steps the indicator has experienced over time. Each selected level of an indicator is related also to the time when it was achieved and two generic statistical measures, S-time-distance and S-time-step, can be calculated from such time matrix. Time distance and time step bring new semantics to policy debates and management decisions.

There are many possible applications in general. As one of the interesting possible applications with respect to SOFI data could be: 
  1. Describing and comparing systemic SOFI over many units and many years, 
  2. Describing and comparing various scenarios also with time distance measures, 
  3. Time distance monitoring differences between actual and expected developments.

The analysis techniques described are not forecasting tools in themselves but provide a means of presentation of complex data sets that have universal appeal, are intuitively understandable and can be usefully applied to a wide variety of substantive fields at macro and micro levels. They are a new way of presenting and analysing indicators complementing and not replacing existing methods.

Where is Slovenia? (Kje je Slovenija?)
Tuesday, 09 August 2011

Interview of Professor Pavle Sicherl in the leading Slovenian newspaper DELO, August 8, 2011

In the interview the time distance innovation and applications in economics and statistics were explained. The main conclusions on the position of Slovenia and Europe are from the article 'Kje je Slovenija?' in the proceedings of the symposium at the Slovenian Academy of Sciences and Arts at the 10th anniverasry of death of Professor Aleksander Bajt.

Some visualisation examples for descriptive statistics
Sunday, 19 June 2011

How to make comparisons of indicators more understandable to policy makers, managers, media and to general public by complementing with the time distance perspective 

Two obvious generic directions of time series comparison - by time and by level 

Besides the levels of the variable (indicator) the two most widely used measures for comparisons are growth rate and static difference between two or more units. However, at the same level of generality there exist two companion generic statistical measures S-time- distance and S-time-step as a special category of time distances defined by the level of the variable. The first measures the distance (proximity) in time between the points in time when the two series compared reach a specified level of the indicator X; the second is an additional measure of dynamics measuring time that was needed to reach the next level of X. 

The time distance approach brings about two persuasive advantages for extensive practical use. Expressed in time units it is intuitively understood by policymakers, professionals, managers, media and the general public, facilitating their subjective perception about their position in this additional dimension. Another technical and presentation advantage is that time and time distance is comparable across variables, fields of concern, and units of comparison. This makes it an excellent analytical, presentation and communication tool. 

This innovation opens the possibility for simultaneous two-dimensional comparisons of time series data in two specified dimensions: vertically (standard measures of static difference) as well as horizontally (Sicherl time distance), providing a new dimension of analysis to a variety of problems. The following examples do not deal here with the calculation of time distances but focus only on the examples how the visualisation of the time distance results can complement the visualisation of other statistical measures like static indexes to provide a broader understanding in the dynamic context. 

Empirically, the degree of disparity may be very different in static terms and in time distance, which leads to new conclusions and semantics important for policy considerations. Therefore, we need both dimensions, especially when we compare indicators across different domains. The few examples here show the visualisation of time matrix transformation of the usual time series database (i.e. time when a specified level of the variable was achieved in each compared unit), followed by examples for S-time-distance and S-time-step for life expectancy over OECD countries.

Visualisation with the help of selected types of time distance graphs (or S-time-distance and S-time-step tables as well) can be included in publications or in the respective web pages of statistical offices, international and national organisations, NGOs, by media to enhance knowledge and understanding. Seeing with new eyes and telling new stories can facilitate stakeholders to build their perceptions and decisions. Faster application of this complementary methodology by potential imaginative users in numerous fields can be helped by developing of the necessary software. 

The full text includes both schematic tables for transformation of conventional time series tables into corresponding time distance companions and the respective empirical examples.
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