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Stochastic sensitivity analysis of concentration measures

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    $a 20170221d2017 m y slo 03 ba
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    $a Stochastic sensitivity analysis of concentration measures $f Martin Boďa
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    $a © 2017 Springer-Verlag Berlin HeidelbergThe paper extends the traditional approach to measuring market concentration by embracing an element of stochasticity that should reflect the analyst’s uncertainty associated with the future development regarding concentration on the market. Whereas conventional practice relies on deterministic assessments of a market concentration measure with the use of current market shares, this says nothing about possible changes that may happen even in a near future. The paper proposes to model the analyst’s beliefs by dint of a suitable joint probability distribution for future market shares and demonstrates how this analytic framework may be employed for regulatory purposes. A total of four candidates for the joint probability distribution of market shares are considered—the Dirichlet distribution, the conditional normal distribution, the Gaussian copula with conditional beta marginals and the predictive distribution arising from the market share attracti
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    $1 001 umb_un_cat*0289902 $1 200 1 $a Central European Journal of Operations Research $v Vol. 25, no. 2 (2017), pp. 441-471 $1 210 $a Berlin $c Springer Nature $d 2017 $1 011 $a 1435-246X $1 011 $a 1613-9178
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Number of the records: 1  

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