| |
| |
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 | |