SAS for Monte Carlo Studies: A Guide for Quantitative Researchers (Paperback)

by Xitao Fan; Akos Felsovalyi; Stephen A. Sivo; Sean C. Keenan (Author) "What is a Monte Carlo study?..." (more)
Key Phrases: generating sample data, matrix decomposition procedure, simulation design information, Corrected Beta Weight, Squared Autoregression Weight, Procedure Variable (more...)

Editorial Reviews

Review
Although accessible to a wide range of SAS users, even experienced users will learn clever new tricks for data generation, management and analysis. This is a wonderful resource for anyone considering the use of Monte Carlo simulation methodology in SAS. --Patrick J. Curran, Ph.D., University of North Carolina at Chapel Hill

This book provides an excellent introduction to using SAS® to conduct Monte Carlo simulation studies at a basic level. -- George A. Marcoulides, Ph.D. Editor, Structural Equation Modeling Journal

This book provides an excellent introduction to using the SAS System to conduct Monte Carlo simulation studies in a variety of substantive areas. Although the book is written at a fairly basic level, it skillfully previews many of the underlying statistical theories necessary for understanding the techniques discussed. --George A. Marcoulides, Ph.D., Editor, Structural Equation Modeling Journal

This is a wonderful resource for anyone considering the use of Monte Carlo simulation methodology in SAS®. -- Patrick J. Curran, Ph.D. University of North Carolina at Chapel Hill

Product Description
With the advance of computing technology, Monte Carlo simulation research has become increasingly popular among quantitative researchers in a variety of disciplines. More and more, statistical methods are being subjected to rigorous empirical scrutiny in the form of statistical simulation so that their limitations and strengths can be understood. With the combination of powerful built-in statistical procedures and versatile programming capabilities, the SAS System is ideal for conducting Monte Carlo simulation research! SAS for Monte Carlo Studies: A Guide for Quantitative Researchers provides a detailed and practical guide for conducting Monte Carlo studies using the SAS System. Quantitative researchers will find this book attractive for its practicality and for its many hands-on application examples of Monte Carlo research.

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Product Details

• Paperback: 272 pages
• Publisher: SAS Publishing (January 1, 2003)
• Language: English
• ISBN-10: 1590471415
• ISBN-13: 978-1590471418
• Product Dimensions: 10.8 x 8.5 x 0.6 inches
• Shipping Weight: 1.4 pounds (View shipping rates and policies)
• Average Customer Review: 4.7 out of 5 stars See all reviews (3 customer reviews)
• Amazon.com Sales Rank: #234,754 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

2 of 2 people found the following review helpful:

5.0 out of 5 stars An appealing introduction for beginners, February 18, 2007

By	Dimitri Shvorob (Nashville, Tennessee) - See all my reviews (REAL NAME)

Do you know how to generate a (univariate) random normal sample in SAS? How about a sample from the multivariate normal distribution, with an arbitrary correlation matix? How about a sample from gamma, or Poisson distribution, or random variates with given 3rd and 4th moments? How about generating an ARMA series?

If not, the authors will show you how, and provide many examples. If you do, $40 can find better application. Speaking for myself, I was hoping to see a SAS-aided discussion of 'smart' MC techniques, and perhaps of resampling methods. No luck.

3 of 4 people found the following review helpful:

4.0 out of 5 stars Concise and helpful, September 14, 2006

By	Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews (TOP 100 REVIEWER)    (REAL NAME)

SAS is still the dominant programming tool in finance despite strong competition from other software packages like Maple and Mathematica. And it remains so despite it being embedded in the paradigm of procedural programming, while these other two packages are representatives of the functional approach to programming. Taste and historical precedence play a large role in the selection of a programming tool, and no doubt part of the reasons for the predominance of SAS. But whatever the reason, SAS is here to stay in the financial community, and of course so are Monte Carlo simulations, which are the topic of this short guide.

As the authors of this book show, Monte Carlo simulations are quite easy to implement in SAS, and one will find within its covers many examples of how to code these simulations in SAS. Due to its date of publication, the code of course reflects an earlier version of SAS, but the differences between it and the current version are small and can be dealt with for the purposes at hand. Monte Carlo simulations have the advantage of being conceptually simple but can take long periods of time to complete. They are usually used to get an understanding of the true population when real data is unavailable or expensive to obtain. There are a few firms on Wall Street that have made it their living to offer packages that allow more "intelligent" implementations of Monte Carlo. These approaches are proprietary of course but are no doubt based on some unique approach to doing variance reduction or importance sampling. One will not find examples of these implementations in this book, but there is plenty of material to keep one busy if one is learning Monte Carlo or SAS, or both, or if one is an expert on Monte Carlo and is teaching it to a class of SAS users. The examples included are straightforward and relatively error free, and one can be readily put into SAS or modified as one sees fit.

5.0 out of 5 stars Extremely Helpful, September 13, 2007

By	Sharat Sharma (PA, USA) - See all my reviews (REAL NAME)

I found this book extremely helpful for handling stochasticity with SAS/STAT environment. I have been working on spreadsheets (simulation add-ins) until now for financial modeling but obviously Excel has got limits. SAS has got the best of both worlds - you can write codes for advanced statistics and model a project capturing stochastic and deterministic variables which is why it is better choice than VBA+Excel+Add-ins(like @RISK). Apart from that book is very well written. It is always a pleasure to read scripts of creators themselves. For people in finance this is very helpful if purchased alongwith 'Using SAS in Financial Research'.