SAS for Monte Carlo Studies A Guide for Quantitative Researchers

ISBN-10: 1590471415

ISBN-13: 9781590471418

Edition: 2003

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Book details

List price: $50.95
Copyright year: 2003
Publisher: SAS Institute
Binding: Hardcover
Pages: 272
Size: 8.25" wide x 10.50" long x 0.50" tall
Weight: 1.386
Language: English

Acknowledgments
Introduction
Introduction
What Is a Monte Carlo Study?
Simulating the Rolling of a Die Twice
Why Is Monte Carlo Simulation Often Necessary?
What Are Some Typical Situations Where a Monte Carlo Study Is Needed?
Assessing the Consequences of Assumption Violations
Determining the Sampling Distribution of a Statistic That Has No Theoretical Distribution
Why Use the SAS System for Conducting Monte Carlo Studies?
About the Organization of This Book
References
Basic Procedures for Monte Carlo Simulation
Introduction
Asking Questions Suitable for a Monte Carlo Study
Designing a Monte Carlo Study
Simulating Pearson Correlation Coefficient Distributions
Generating Sample Data
Generating Data from a Distribution with Known Characteristics
Transforming Data to Desired Shapes
Transforming Data to Simulate a Specified Population Inter-variable Relationship Pattern
Implementing the Statistical Technique in Question
Obtaining and Accumulating the Statistic of Interest
Analyzing the Accumulated Statistic of Interest
Drawing Conclusions Based on the MC Study Results
Summary
Generating Univariate Random Numbers in SAS
Introduction
RANUNI, the Uniform Random Number Generator
Uniformity (the EQUIDST Macro)
Randomness (the CORRTEST Macro)
Generating Random Numbers with Functions versus CALL Routines
Generating Seed Values (the SEEDGEN Macro)
List of All Random Number Generators Available in SAS
Examples for Normal and Lognormal Distributions
Random Sample of Population Height (Normal Distribution)
Random Sample of Stock Prices (Lognormal Distribution)
The RANTBL Function
Examples Using the RANTBL Function
Random Sample of Bonds with Bond Ratings
Generating Random Stock Prices Using the RANTBL Function
Summary
References
Generating Data in Monte Carlo Studies
Introduction
Generating Sample Data for One Variable
Generating Sample Data from a Normal Distribution with the Desired Mean and Standard Deviation
Generating Data from Non-Normal Distributions
Using the Generalized Lambda Distribution (GLD) System
Using Fleishman's Power Transformation Method
Generating Sample Data from a Multivariate Normal Distribution
Generating Sample Data from a Multivariate Non-Normal Distribution
Examining the Effect of Data Non-normality on Inter-variable Correlations
Deriving Intermediate Correlations
Converting between Correlation and Covariance Matrices
Generating Data That Mirror Your Sample Characteristics
Summary
References
Automating Monte Carlo Simulations
Introduction
Steps in a Monte Carlo Simulation
The Problem of Matching Birthdays
The Seed Value
Monitoring the Execution of a Simulation
Portability
Automating the Simulation
A Macro Solution to the Problem of Matching Birthdays
Full-Time Monitoring with Macros
Simulation of the Parking Problem (Renyi's Constant)
Summary
References
Conducting Monte Carlo Studies That Involve Univariate Statistical Techniques
Introduction
Example 1: Assessing the Effect of Unequal Population Variances in a T-Test
Computational Aspects of T-Tests
Design Considerations
Different SAS Programming Approaches
T-Test Example: First Approach
T-Test Example: Second Approach
Example 2: Assessing the Effect of Data Non-Normality on the Type I Error Rate in ANOVA
Design Considerations
ANOVA Example Program
Example 3: Comparing Different R[superscript 2] Shrinkage Formulas in Regression Analysis
Different Formulas for Correcting Sample R[superscript 2] Bias
Design Considerations
Regression Analysis Sample Program
Summary
References
Conducting Monte Carlo Studies for Multivariate Techniques
Introduction
Example 1: A Structural Equation Modeling Example
Descriptive Indices for Assessing Model Fit
Design Considerations
SEM Fit Indices Studied
Design of Monte Carlo Simulation
Deriving the Population Covariance Matrix
Dealing with Model Misspecification
SEM Example Program
Some Explanations of Program 7.2
Selected Results from Program 7.2
Example 2: Linear Discriminant Analysis and Logistic Regression for Classification
Major Issues Involved
Design
Data Source and Model Fitting
Example Program Simulating Classification Error Rates of PDA and LR
Some Explanations of Program 7.3
Selected Results from Program 7.3
Summary
References
Examples for Monte Carlo Simulation in Finance: Estimating Default Risk and Value-at-Risk
Introduction
Example 1: Estimation of Default Risk
Example 2: VaR Estimation for Credit Risk
Example 3: VaR Estimation for Portfolio Market Risk
Summary
References
Modeling Time Series Processes with SAS/ETS Software
Introduction to Time Series Methodology
Box and Jenkins ARIMA Models
Akaike's State Space Models for Multivariate Times Series
Modeling Multiple Regression Data with Serially Correlated Disturbances
Introduction to SAS/ETS Software
Example 1: Generating Univariate Time Series Processes
Example 2: Generating Multivariate Time Series Processes
Example 3: Generating Correlated Variables with Autocorrelated Errors
Example 4: Monte Carlo Study of How Autocorrelation Affects Regression Results
Summary
References
Index
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