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Prediction of Voting Pattern

In: Applications of Regression Techniques

Author

Listed:
  • Manoranjan Pal

    (Indian Statistical Institute, Economic Research Unit)

  • Premananda Bharati

    (Indian Statistical Institute, Biological Anthropology Unit)

Abstract

In a setup of a given set of political parties in a region it is possible to build up a Markov chain model, which enables us to predict the results of the subsequent election. We assume that the result of the latest election depends only on the previous election. The transition probabilities can be interpreted as the coefficients of a set of regression lines, where the number of votes obtained by each party in the latest election is regressed on the number of votes obtained by the parties in the previous election. This demands the coefficients to be non-negative. The problem is that the transition probabilities are not known here and need to be estimated from data. We apply the above model to predict the results of the next election of West Bengal using the results of the latest two elections. Least Squares estimates are found subject to non-negative constraints of the coefficients. We try to assess the prospect of Bharatiya Janata Party (BJP) in West Bengal and get mixed results.

Suggested Citation

  • Manoranjan Pal & Premananda Bharati, 2019. "Prediction of Voting Pattern," Springer Books, in: Applications of Regression Techniques, chapter 0, pages 85-103, Springer.
    • Handle: RePEc:spr:sprchp:978-981-13-9314-3_5
    DOI: 10.1007/978-981-13-9314-3_5
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