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Information content of survey data: applications of entropy and dissimilarity measures

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  • Emilia Tomczyk

    (Warsaw School of Economics)

Abstract

This paper evaluates information content of survey data by the means of entropy and dissimilarity measures. Similarities between a priori information (expectations) and a posteriori information (realizations) are assessed for four variables originating from Polish business tendency survey and analyzed across two ownership sectors and four size categories. The measures employed include Shannon empirical entropy, Kullback-Leibler relative entropy, and Chomatowski-Soko³owski dissimilarity coefficient. Results of empirical analysis allow to conclude that enterprise size does not significantly affect entropy values even though some size effects are observed in case of production and general business conditions variables. Public and private enterprises are not differentiated by interpretation of expectations horizon and therefore can be studied on the aggregated level without loss of important information on forecast horizon patterns. Additionally, previous findings (see E. Tomczyk, 2011) are generally confirmed on longer sample. Production time series are found to be characterized by the highest entropy, and prices data – the lowest; entropy of production is also found to be the least variable. In public enterprises, concentration of answers to the survey questions is higher and also more variable than in private sector.

Suggested Citation

  • Emilia Tomczyk, 2012. "Information content of survey data: applications of entropy and dissimilarity measures," Working Papers 62, Department of Applied Econometrics, Warsaw School of Economics.
    • Handle: RePEc:wse:wpaper:62
    as

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    References listed on IDEAS

    as
    1. World Commission on Environment and Development,, 1987. "Our Common Future," OUP Catalogue, Oxford University Press, number 9780192820808.
    2. Emilia Tomczyk, 2010. "Application of measures of entropy, information content and dissimilarity of structures to business tendency survey data," Working Papers 47, Department of Applied Econometrics, Warsaw School of Economics.
    3. Sen, Amartya, 1997. "On Economic Inequality," OUP Catalogue, Oxford University Press, number 9780198292975.
    4. Koen Frenken & Alessandro Nuvolari, 2004. "Entropy statistics as a framework to analyse technological evolution," Chapters, in: John Foster & Werner Hölzl (ed.), Applied Evolutionary Economics and Complex Systems, chapter 5, Edward Elgar Publishing.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    tendency surveys; expectations; entropy; dissimilarity of structures;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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