Bootstrapped insights into empirical applications of stochastic dominance

Article Abstract:

Bootstrapping is a statistical method that enhances the understanding and reliability of empirical uses of stochastic dominance. The technique provides data about empirical distribution function (EDF) performance which rekindles the potential empirical usages of stochastic dominance, functioning in a complementary fashion with EDF by: (1) assisting decision makers in finding uncertain order statistics by approximating biases and standard deviations, and (2) smoothing order statistics to bring about a pronounced increase in algorithm power in dominance-evident populations. Bootstrapping as a feasible alternative to the EDF is verified by its power in cumulative density function analyses.

Research, Methods, Usage, Stochastic analysis, Monte Carlo method, Monte Carlo methods, Distribution (Probability theory)

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Output decision under demand uncertainty with stochastic production function: a contingent claims approach

Article Abstract:

A contingent claims analysis is presented for the output decisions of a firm facing uncertainty of demand and production technology. The analysis indicates that the optimal output level rises with the higher interest rate under uncertainty, that demand volatility and production lead time can have either a positive or a negative impact on the optimal output level, that increased demand volatility decreases the optimal project value, and that the optimal project value declines with the longer production lead time and the higher interest rate under demand uncertainty.

Management science, Models, Analysis, Stochastic processes, Production functions (Economics), Uncertainty (Information theory)

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