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
11 - The Measurement Box Model
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
It has generally been customary certainly to regard as an axiom the hypothesis that if any quantity has been determined by several direct observations, made under the same circumstances and with equal care, the arithmetical mean of the observed values affords the most probable value. …
Carl Friedrich GaussIntroduction
Regression is the dominant method of empirical analysis in economics. It has two basic applications: description and inference. The first eight chapters of this book use regression for description. Chapters 9 and 10 introduce and review tools for making statistical inference. We are now ready to see how regression is used when the data are a sample from a population.
The next few chapters prepare the ground for the study of regression as a tool for inference and forecasting. Inference in general means reasoning from factual knowledge or evidence. In statistics, we have a sample drawn from a population and use the sample to infer something about the population.
For example, suppose we have data on 1,178 people in the United States in 1989 selected at random from the adult working population. We have the level of experience and the wages of these people. Part 1 discusses the use of regression to provide a summary of the bivariate wage-experience data. Statistical inference aims at a much more ambitious goal. Instead of simply describing the relationship for those 1,178 people, we wish to discover the relationship between wage and experience for all of the adult workers in the United States.
- Type
- Chapter
- Information
- Introductory EconometricsUsing Monte Carlo Simulation with Microsoft Excel, pp. 281 - 302Publisher: Cambridge University PressPrint publication year: 2005