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SAS Institute Inc.
Sas For Monte Carlo Studies

Sas For Monte Carlo Studies

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Product Details

ISBN-13: 9781590471418
Publisher: SAS Institute Inc.
Publication date: 01/12/2009
Edition description: New Edition
Pages: 272
Product dimensions: 8.25(w) x 11.00(h) x 0.57(d)

Table of Contents

Chapter 1Introduction
1.2What Is a Monte Carlo Study?2
1.2.1Simulating the Rolling of a Die Twice2
1.3Why Is Monte Carlo Simulation Often Necessary?4
1.4What Are Some Typical Situations Where a Monte Carlo Study Is Needed?5
1.4.1Assessing the Consequences of Assumption Violations5
1.4.2Determining the Sampling Distribution of a Statistic That Has No Theoretical Distribution6
1.5Why Use the SAS System for Conducting Monte Carlo Studies?7
1.6About the Organization of This Book8
Chapter 2Basic Procedures for Monte Carlo Simulation
2.2Asking Questions Suitable for a Monte Carlo Study12
2.3Designing a Monte Carlo Study13
2.3.1Simulating Pearson Correlation Coefficient Distributions13
2.4Generating Sample Data16
2.4.1Generating Data from a Distribution with Known Characteristics16
2.4.2Transforming Data to Desired Shapes17
2.4.3Transforming Data to Simulate a Specified Population Inter-variable Relationship Pattern17
2.5Implementing the Statistical Technique in Question17
2.6Obtaining and Accumulating the Statistic of Interest18
2.7Analyzing the Accumulated Statistic of Interest19
2.8Drawing Conclusions Based on the MC Study Results22
Chapter 3Generating Univariate Random Numbers in SAS
3.2RANUNI, the Uniform Random Number Generator26
3.3Uniformity (the EQUIDST Macro)27
3.4Randomness (the CORRTEST Macro)30
3.5Generating Random Numbers with Functions versus CALL Routines34
3.6Generating Seed Values (the SEEDGEN Macro)38
3.7List of All Random Number Generators Available in SAS39
3.8Examples for Normal and Lognormal Distributions45
3.8.1Random Sample of Population Height (Normal Distribution)45
3.8.2Random Sample of Stock Prices (Lognormal Distribution)46
3.9The RANTBL Function51
3.10Examples Using the RANTBL Function52
3.10.1Random Sample of Bonds with Bond Ratings52
3.10.2Generating Random Stock Prices Using the RANTBL Function54
Chapter 4Generating Data in Monte Carlo Studies
4.2Generating Sample Data for One Variable60
4.2.1Generating Sample Data from a Normal Distribution with the Desired Mean and Standard Deviation60
4.2.2Generating Data from Non-Normal Distributions62 the Generalized Lambda Distribution (GLD) System62 Fleishman's Power Transformation Method66
4.3Generating Sample Data from a Multivariate Normal Distribution71
4.4Generating Sample Data from a Multivariate Non-Normal Distribution79
4.4.1Examining the Effect of Data Non-normality on Inter-variable Correlations80
4.4.2Deriving Intermediate Correlations82
4.5Converting between Correlation and Covariance Matrices87
4.6Generating Data That Mirror Your Sample Characteristics90
Chapter 5Automating Monte Carlo Simulations
5.2Steps in a Monte Carlo Simulation94
5.3The Problem of Matching Birthdays94
5.4The Seed Value98
5.5Monitoring the Execution of a Simulation98
5.7Automating the Simulation100
5.8A Macro Solution to the Problem of Matching Birthdays101
5.9Full-Time Monitoring with Macros103
5.10Simulation of the Parking Problem (Renyi's Constant)105
Chapter 6Conducting Monte Carlo Studies That Involve Univariate Statistical Techniques
6.2Example 1: Assessing the Effect of Unequal Population Variances in a T-Test118
6.2.1Computational Aspects of T-Tests119
6.2.2Design Considerations119
6.3.3Different SAS Programming Approaches120
6.3.4T-Test Example: First Approach121
6.3.5T-Test Example: Second Approach125
6.3Example 2: Assessing the Effect of Data Non-Normality on the Type I Error Rate in ANOVA129
6.3.1Design Considerations130
6.3.2ANOVA Example Program130
6.4Example 3: Comparing Different R[superscript 2] Shrinkage Formulas in Regression Analysis136
6.4.1Different Formulas for Correcting Sample R[superscript 2] Bias136
6.4.2Design Considerations137
6.4.3Regression Analysis Sample Program138
Chapter 7Conducting Monte Carlo Studies for Multivariate Techniques
7.2Example 1: A Structural Equation Modeling Example146
7.2.1Descriptive Indices for Assessing Model Fit146
7.2.2Design Considerations147
7.2.3SEM Fit Indices Studied148
7.2.4Design of Monte Carlo Simulation148 the Population Covariance Matrix150 with Model Misspecification151
7.2.5SEM Example Program152
7.2.6Some Explanations of Program 7.2155
7.2.7Selected Results from Program 7.2160
7.3Example 2: Linear Discriminant Analysis and Logistic Regression for Classification161
7.3.1Major Issues Involved161
7.3.3Data Source and Model Fitting164
7.3.4Example Program Simulating Classification Error Rates of PDA and LR165
7.3.5Some Explanations of Program 7.3168
7.3.6Selected Results from Program 7.3172
Chapter 8Examples for Monte Carlo Simulation in Finance: Estimating Default Risk and Value-at-Risk
8.2Example 1: Estimation of Default Risk179
8.3Example 2: VaR Estimation for Credit Risk185
8.4Example 3: VaR Estimation for Portfolio Market Risk199
Chapter 9Modeling Time Series Processes with SAS/ETS Software
9.1Introduction to Time Series Methodology213
9.1.1Box and Jenkins ARIMA Models213
9.1.2Akaike's State Space Models for Multivariate Times Series216
9.1.3Modeling Multiple Regression Data with Serially Correlated Disturbances216
9.2Introduction to SAS/ETS Software216
9.3Example 1: Generating Univariate Time Series Processes218
9.4Example 2: Generating Multivariate Time Series Processes221
9.5Example 3: Generating Correlated Variables with Autocorrelated Errors228
9.6Example 4: Monte Carlo Study of How Autocorrelation Affects Regression Results234

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