Monte Carlo Spreadsheet Simulation using Resampling

• Main Author: LEONG, Thin Yin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2007
• Subjects: Monte Carlo simulation; spreadsheet; resampling; Computer Sciences; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1196; http://dx.doi.org/10.1287/ited.7.3.188
• Institution: Singapore Management University

The ubiquitous spreadsheet can be used to model situations with random values, in what is commonly referred to as Monte Carlo simulation. For simple cases, adding random functions (like ExcelTM’s RAND) is enough. In general business models, complex inverse distribution functions, in combination with RAND, are needed to generate the right random values. But first the modeler must determine the appropriate best-fit distribution to use. This can be a daunting process for undergraduates and typical executives. So for expediency, simulation add-ins (with the additional learning time and possible costs) may be employed. The use of add-ins however makes the modeling less transparent. A more direct alternative is to resample the raw data, which in many cases are not sufficient in numbers to establish statistical goodness of fit. This paper reviews the limitations of current spreadsheet resampling methods and proposes new simple yet effective formulations that better accommodate the classroom and practical real-world applications.