Book contents
- Frontmatter
- Contents
- Preface
- User Guide
- 1 Introduction
- PART 1 DESCRIPTION
- PART 2 INFERENCE
- 9 Monte Carlo Simulation
- 10 Review of Statistical Inference
- 11 The Measurement Box Model
- 12 Comparing Two Populations
- 13 The Classical Econometric Model
- 14 The Gauss–Markov Theorem
- 15 Understanding the Standard Error
- 16 Confidence Intervals and Hypothesis Testing
- 17 Joint Hypothesis Testing
- 18 Omitted Variable Bias
- 19 Heteroskedasticity
- 20 Autocorrelation
- 21 Topics in Time Series
- 22 Dummy Dependent Variable Models
- 23 Bootstrap
- 24 Simultaneous Equations
- Glossary
- Index
9 - Monte Carlo Simulation
from PART 2 - INFERENCE
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- User Guide
- 1 Introduction
- PART 1 DESCRIPTION
- PART 2 INFERENCE
- 9 Monte Carlo Simulation
- 10 Review of Statistical Inference
- 11 The Measurement Box Model
- 12 Comparing Two Populations
- 13 The Classical Econometric Model
- 14 The Gauss–Markov Theorem
- 15 Understanding the Standard Error
- 16 Confidence Intervals and Hypothesis Testing
- 17 Joint Hypothesis Testing
- 18 Omitted Variable Bias
- 19 Heteroskedasticity
- 20 Autocorrelation
- 21 Topics in Time Series
- 22 Dummy Dependent Variable Models
- 23 Bootstrap
- 24 Simultaneous Equations
- Glossary
- Index
Summary
Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin.
John von NeumannThe one thing about Monte Carlo is that it never gives an exact answer.
Stanislaw UlamIntroduction
The chapters in the first part of this book make clear that regression analysis can be used to describe data. The remainder of this book is dedicated to understanding regression as a tool for drawing inferences about how variables are related to each other. The central idea in inferential statistics is that the data we observe are just one sample from a larger population. The goal of inference is to determine what evidence the sample provides about the relationship between variables in the population.
This chapter explains how we will use the computer to draw random samples to evaluate the performance of a variety of sample-based statistics. We will review basic theory behind random number generation with computers, offer a simple example of Monte Carlo simulation, and introduce a Monte Carlo simulation Excel add-in.
Like regression analysis, Monte Carlo simulation is a general term that has many meanings. The word “simulation” signifies that we build an artificial model of a real system to study and understand the system. The “Monte Carlo” part of the name alludes to the randomness inherent in the analysis:
The name “Monte Carlo” was coined by [physicist Nicholas] Metropolis (inspired by [Stanislaw] Ulam's interest in poker) during the Manhattan Project of World War II, because of the similarity of statistical simulation to games of chance, and because the capital of Monaco was a center for gambling and similar pursuits. […]
- Type
- Chapter
- Information
- Introductory EconometricsUsing Monte Carlo Simulation with Microsoft Excel, pp. 215 - 237Publisher: Cambridge University PressPrint publication year: 2005
- 1
- Cited by