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A Binomial Distribution With Dependent Trials And Its Use in Stochastic Model Evaluation

Author

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  • Xekalaki, Evdokia
  • Panaretos, John

Abstract

A model of Markov dependent trials is considered that leads to a generalization of the binomial distribution in the context of evaluating models of a time series by exploiting the sequential nature of model-based predictions. Adopting an evaluation method similar in nature to that suggested by Xekalaki & Katti (1984), the behaviour of the model is assigned a score that reflects the concordance or discordance of predicted and observed values for each of a sequence of points in time. The resulting series of scores leads to a final rating which is considered as a measure of the predictive ability of the model. The Markov dependent distribution is used to develop exact theory for the construction of confidence intervals and for testing hypotheses pertaining to the forecasting protential of a model. Some asymptotic theory is also developed.

Suggested Citation

  • Xekalaki, Evdokia & Panaretos, John, 2004. "A Binomial Distribution With Dependent Trials And Its Use in Stochastic Model Evaluation," MPRA Paper 6393, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:6393
    as

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    File URL: https://mpra.ub.uni-muenchen.de/6393/1/MPRA_paper_6393.pdf
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    References listed on IDEAS

    as
    1. Xekalaki, Evdokia & Panaretos, John & Psarakis, Stelios, 2003. "A Predictive Model Evaluation and Selection Approach - The Correlated Gamma Ratio Distribution," MPRA Paper 6389, University Library of Munich, Germany.
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    More about this item

    Keywords

    Model evaluation; Model validation; Dependent Bernouli trials; Forecasting models;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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