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Just published: New book on time distance by Professor Pavle Sicherl
Thursday, 01 March 2012

Book: Time Distance in Economics and Statistics - New Insights from Existing Data

The book on time distance methodology by Professor Pavle Sicherl was published in Vienna. The time perspective, which no doubt exists in human perception when comparing different situations, is systematically introduced in comparative analysis both as a concept and as a quantifiable measure. Time distance is an innovative approach for looking at time-series data, it offers two improvements in the present state-of-the-art of comparative analysis.

The first one is analytical and statistical – two novel generic statistical measures S-time-distance and S-time-step are generalised to complement conventional measures in time series comparisons, regressions, models, forecasting and monitoring, and to provide from existing data new insights due to an added dimension of analysis. Expressed in time units they are intuitively understandable; they can be compared across variables, fields of concern, and units of comparison.

The second component is normative and theoretical, related to subjective perceptions, policy and welfare issues. Time distance concept can influence the perception and decisions of people when they are assessing their relative position in the society and across countries over time. Concept of the ‘overall degree of disparity’ combines static and time distance measures of disparity with the potential to bring new understanding in economics, management, research and statistics. Empirical applications analyse time distance differences between countries in the world, OECD and EU, regional disparities, transition depression, ICT and digital divide, and monitoring implementation of UN MDGs and Lisbon strategy in the EU.

OECD Statistics Working Papers, 2011/09 on time distance
Sunday, 08 January 2012

New understanding and insights from time-series data based on two generic measures: S-time-distance and S-time-step

Professor Pavle Sicherl prepared a methodological paper on time distance approach that was published by OECD Publishing. It covers several additional technical points and application examples of the time distance method beyond earlier applications. The time distance approach presents a means of presentation of complex data sets that have universal appeal; it is intuitively understandable and can be usefully applied to a wide variety of substantive fields at macro and micro levels. It represents a new way of presenting and analysing indicators complementing and not replacing existing methods. 

In summary, time distance is an innovative approach for looking at time-series data. Expressed in time units, the approach is easy to understand and provides a useful complement to existing methods. The time distance approach compares time series in the horizontal dimension, i.e. for a given level of the variable, based on two generic statistical measures: S-time-distance and S-time-step. These measures are based on a time matrix that summarises information over many units and years and that provides a first-level visualization tool. The paper also introduces the concept of the ‘overall degree of disparity’, defined as proximity in the indicator space as well as in time, arguing that this concept has the potential to bring new understanding in economics, management, research and statistics. 

While the OECD ‘Your Better Life Index’ is a tool that allows addressing differences in subjective opinions among fields of concern and indicators, the time distance concept opens the question about the weight that people assign to the two dimensions of disparity discussed in this paper (static measure and time distance) to arrive at a overall evaluation of their position in society and globally. 

The empirical examples included in this paper demonstrate how the method could be applied to three indicators (life expectancy at birth, the share of the elderly population, and projections of population growth) drawn from the OECD Factbook. These examples were drawn from an earlier presentation by the author referring to 14 variables (‘Visualisation of 50 years of OECD countries at a glance’, which is available on and on ‘50 years of OECD countries at a glance’). The paper has also applied the methodology for monitoring Millennium Development Goals across many indicators, either for the world regions or at the country level. 

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.

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