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Auto-Havra Charvat entropic measures for stationary time series of categorical data

Author

Listed:
  • Biswas, Atanu
  • Maria del Carmen Pardo
  • Guha, Apratim

Abstract

For stationary time series of nominal categorical data or ordinal categorical data (with arbitrary ordered numberings of the categories), autocorrelation does not make much sense. One can alternatively think of using some entropic measures, of which a measure introduced by Havrda and Charvat (1967) could be particularly useful. We discuss some theoretical properties of measures from this class in the context of categorical time series and look at specific examples. Theoretical properties and simulation results are given along with an illustrative real data example.

Suggested Citation

  • Biswas, Atanu & Maria del Carmen Pardo & Guha, Apratim, 2013. "Auto-Havra Charvat entropic measures for stationary time series of categorical data," IIMA Working Papers WP2013-05-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    • Handle: RePEc:iim:iimawp:12102
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