Libro Sasr For Monte Carlo Studies de Fan, Xitao/ Felsovalyi, Akos/ Sivo, Stephen A./ Keenan, Sean C.

Tabla de Contenidos del libro

Acknowledgments

vii

Introduction

(1)

What Is a Monte Carlo Study?

(2)

Simulating the Rolling of a Die Twice

(2)

Why Is Monte Carlo Simulation Often Necessary?

(1)

What Are Some Typical Situations Where a Monte Carlo Study Is Needed?

(2)

Assessing the Consequences of Assumption Violations

(1)

Determining the Sampling Distribution of a Statistic That Has No Theoretical Distribution

(1)

Why Use the SAS System for Conducting Monte Carlo Studies?

(1)

About the Organization of This Book

(1)

References

(2)

Basic Procedures for Monte Carlo Simulation

Introduction

(1)

Asking Questions Suitable for a Monte Carlo Study

(1)

Designing a Monte Carlo Study

(3)

Simulating Pearson Correlation Coefficient Distributions

(3)

Generating Sample Data

(1)

Generating Data from a Distribution with Known Characteristics

(1)

Transforming Data to Desired Shapes

(1)

Transforming Data to Simulate a Specified Population Inter-variable Relationship Pattern

(1)

Implementing the Statistical Technique in Question

(1)

Obtaining and Accumulating the Statistic of Interest

(1)

Analyzing the Accumulated Statistic of Interest

(3)

Drawing Conclusions Based on the MC Study Results

(1)

Summary

(2)

Generating Univariate Random Numbers in SAS

Introduction

(1)

RANUNI, the Uniform Random Number Generator

(1)

Uniformity (the EQUIDST Macro)

(3)

Randomness (the CORRTEST Macro)

(4)

Generating Random Numbers with Functions versus CALL Routines

(4)

Generating Seed Values (the SEEDGEN Macro)

(1)

List of All Random Number Generators Available in SAS

(6)

Examples for Normal and Lognormal Distributions

(6)

Random Sample of Population Height (Normal Distribution)

(1)

Random Sample of Stock Prices (Lognormal Distribution)

(5)

The RANTBL Function

(1)

Examples Using the RANTBL Function

(5)

Random Sample of Bonds with Bond Ratings

(2)

Generating Random Stock Prices Using the RANTBL Function

(3)

Summary

(1)

References

(1)

Generating Data in Monte Carlo Studies

Introduction

(1)

Generating Sample Data for One Variable

(11)

Generating Sample Data from a Normal Distribution with the Desired Mean and Standard Deviation

(2)

Generating Data from Non-Normal Distributions

(1)

Using the Generalized Lambda Distribution (GLD) System

(4)

Using Fleishman's Power Transformation Method

(5)

Generating Sample Data from a Multivariate Normal Distribution

(8)

Generating Sample Data from a Multivariate Non-Normal Distribution

(8)

Examining the Effect of Data Non-normality on Inter-variable Correlations

(2)

Deriving Intermediate Correlations

(5)

Converting between Correlation and Covariance Matrices

(3)

Generating Data That Mirror Your Sample Characteristics

(1)

Summary

(1)

References

(2)

Automating Monte Carlo Simulations

Introduction

(1)

Steps in a Monte Carlo Simulation

(1)

The Problem of Matching Birthdays

(4)

The Seed Value

(1)

Monitoring the Execution of a Simulation

(2)

Portability

100

(1)

Automating the Simulation

100

(1)

A Macro Solution to the Problem of Matching Birthdays

101

(2)

Full-Time Monitoring with Macros

103

(2)

Simulation of the Parking Problem (Renyi's Constant)

105

(11)

Summary

116

(1)

References

116

(1)

Conducting Monte Carlo Studies That Involve Univariate Statistical Techniques

Introduction

117

(1)

Example 1: Assessing the Effect of Unequal Population Variances in a T-Test

118

(11)

Computational Aspects of T-Tests

119

(1)

Design Considerations

119

(1)

Different SAS Programming Approaches

120

(1)

T-Test Example: First Approach

121

(4)

T-Test Example: Second Approach

125

(4)

Example 2: Assessing the Effect of Data Non-Normality on the Type I Error Rate in ANOVA

129

(7)

Design Considerations

130

(1)

ANOVA Example Program

130

(6)

Example 3: Comparing Different R2 Shrinkage Formulas in Regression Analysis

136

(7)

Different Formulas for Correcting Sample R2 Bias

136

(1)

Design Considerations

137

(1)

Regression Analysis Sample Program

138

(5)

Summary

143

(1)

References

143

(2)

Conducting Monte Carlo Studies for Multivariate Techniques

Introduction

145

(1)

Example 1: A Structural Equation Modeling Example

146

(15)

Descriptive Indices for Assessing Model Fit

146

(1)

Design Considerations

147

(1)

SEM Fit Indices Studied

148

(1)

Design of Monte Carlo Simulation

148

(2)

Deriving the Population Covariance Matrix

150

(1)

Dealing with Model Misspecification

151

(1)

SEM Example Program

152

(3)

Some Explanations of Program 7.2

155

(5)

Selected Results from Program 7.2

160

(1)

Example 2: Linear Discriminant Analysis and Logistic Regression for Classification

161

(12)

Major Issues Involved

161

(1)

Design

162

(2)

Data Source and Model Fitting

164

(1)

Example Program Simulating Classification Error Rates of PDA and LR

165

(3)

Some Explanations of Program 7.3

168

(4)

Selected Results from Program 7.3

172

(1)

Summary

173

(1)

References

174

(3)

Examples for Monte Carlo Simulation in Finance: Estimating Default Risk and Value-at-Risk

Introduction

177

(2)

Example 1: Estimation of Default Risk

179

(6)

Example 2: VaR Estimation for Credit Risk

185

(14)

Example 3: VaR Estimation for Portfolio Market Risk

199

(12)

Summary

211

(1)

References

212

(1)

Modeling Time Series Processes with SAS/ETS Software

Introduction to Time Series Methodology

213

(3)

Box and Jenkins ARIMA Models

213

(3)

Akaike's State Space Models for Multivariate Times Series

216

(1)

Modeling Multiple Regression Data with Serially Correlated Disturbances

216

(1)

Introduction to SAS/ETS Software

216

(2)

Example 1: Generating Univariate Time Series Processes

218

(3)

Example 2: Generating Multivariate Time Series Processes

221

(7)

Example 3: Generating Correlated Variables with Autocorrelated Errors

228

(6)

Example 4: Monte Carlo Study of How Autocorrelation Affects Regression Results

234

(9)

Summary

243

(1)

References

243

(2)

Index

245

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Sasr For Monte Carlo Studies

por Fan, Xitao ; Felsovalyi, Akos ; Sivo, Stephen A. ; Keenan, Sean C.