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10 de dic. de 2022 · Another key task in predictive physics-based simulations affected by uncertainty is understanding the impact of input uncertainty in the simulation’s output of interest, known as (global)...
9 de feb. de 2024 · Our empirical analysis shows that the equity premium appears to be earned for facing uncertainty, especially high uncertainty that is disconnected from lower volatility, rather than for facing volatility as traditionally assumed.
On the one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are dificult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models.
1 de oct. de 2020 · Specifically, we find that imports respond negatively, and in a statistically significant way, and more than output, when there is a shock to a standard uncertainty measure: the VXO stock market option-implied volatility index.
Parameter uncertainties in mathematical models arise from measurement noise associated with uncertain inputs and outputs during experimentation. Consider the case where variances of the output measurements are perfectly known (i.e., ∑ Y is diagonal with known elements).
30 de jun. de 2020 · A wider range means greater uncertainty and implies a higher expected volatility of output growth. The results from Chart 1 demonstrate that when capital and labor are complements, output responds differently depending on current economic conditions.
1 de ene. de 2020 · The methods that quantify input uncertainty in simulation output data can be divided into frequentist and Bayesian approaches based on how the uncertainty around unknown input models and parameters is modeled.
