Monte Carlo Simulation and Resampling Methods for Social Science

ISBN-10: 1452288909
ISBN-13: 9781452288901
Edition: 2014
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Description: Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, this book illustrates abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of  More...

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Book details

Copyright year: 2014
Publisher: SAGE Publications, Incorporated
Publication date: 8/6/2013
Binding: Paperback
Pages: 304
Size: 7.37" wide x 9.12" long x 0.75" tall
Weight: 1.386
Language: English

Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, this book illustrates abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for students learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation.

Acknowledgments
Introduction
Can You Repeat That Please?
Simulation and Resampling Methods
Simulations as Experiments
Simulations Help Develop Intuition
An Overview of Simulation
Resampling Methods as Simulation
OLS as a Motivating Example
Two Brief Examples
Example 1: A Statistical Simulation
Example 2: A Substantive Theory Simulation
Looking Ahead
Assumed Knowledge
A Preview of the Book
R Packages
Probability
Introduction
Some Basic Rules of Probability
Introduction to Set Theory
Properties of Probability
Conditional Probability
Simple Math With Probabilities
Random Variables and Probability Distributions
Discrete Random Variables
Some Common Discrete Distributions
Continuous Random Variables
Two Common Continuous Distributions
Other Continuous Distributions
Conclusions
Introduction to R
Introduction
What Is R?
Resources
Using R With a Text Editor
First Steps
Creating Objects
Basic Manipulation of Objects
Vectors and Sequences
Matrices
Functions
Matrix Algebra Functions
Creating New Functions
Working With Data
Loading Data
Exploring the Data
Statistical Models
Generalized Linear Models
Basic Graphics
Conclusions
Random Number Generation
Introduction
Probability Distributions
Drawing Random Numbers
Creating Your Own Distribution Functions
Systematic and Stochastic
The Systematic Component
The Stochastic Component
Repeating the Process
Programming in R
for Loops
Efficient Programming
If-Else
Completing the OLS Simulation
Anatomy of a Script File
Statistical Simulation of the Linear Model
Introduction
Evaluating Statistical Estimators
Bias, Efficiency, and Consistency
Measuring Estimator Performance in R
Simulations as Experiments
Heteroskedasticity
Multicollinearity
Measurement Error
Omitted Variable
Serial Correlation
Clustered Data
Heavy-Tailed Errors
Conclusions
Simulating Generalized Linear Models
Introduction
Simulating OLS as a Probability Model
Simulating GLMs
Binary Models
Ordered Models
Multinomial Models
Extended Examples
Ordered or Multinomial?
Count Models
Duration Models
Computational Issues for Simulations
Research Computing
Parallel Processing
Conclusions
Testing Theory Using Simulation
Introduction
What Is a Theory?
Zipf's Law
Testing Zipf's Law With Frankenstein
From Patterns to Explanations
Punctuated Equilibrium and Policy Responsiveness
Testing Punctuated Equilibrium Theory
From Patterns to Explanations
Dynamic Learning
Reward and Punishment
Damned If You Do, Damned If You Don't
The Midas Touch
Conclusions
Resampling Methods
Introduction
Permutation and Randomization Tests
A Basic Permutation Test
Randomization Tests
Permutation/Randomization and Multiple Regression Models
Jackknifing
An Example
An Application: Simulating Heteroskedasticity
Pros and Cons of Jackknifing
Bootstrapping
Bootstrapping Basics
Bootstrapping With Multiple Regression Models
Adding Complexity: Clustered Bootstrapping
Conclusions
Other Simulation-Based Methods
Introduction
QI Simulation
Statistical Overview
Examples
Simulating QI With Zelig
Average Case Versus Observed Values
The Benefits of QI Simulation
Cross-Validation
How CV Can Help
An Example
Using R Functions for CV
Conclusions
Final Thoughts
A Summary of the Book
Going Forward
Conclusions
References
Index

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