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
16 - Confidence Intervals and Hypothesis Testing
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
We would not assert that every economist misunderstands statistical significance, only that most do, and these some of the best economic scientists. … Simulation, new data sets, and quantitative thinking about the conversation of the science offer a way forward. The first step anyway is plain: stop searching for economic findings under the lamppost of statistical significance.
Deirdre N. McCloskey and Stephen T. ZiliakIntroduction
This chapter shows how a single sample can be used to construct confidence intervals and test hypotheses about population parameters. Hypothesis testing, also known as testing for significance, is a fundamental part of inferential econometrics.
Statistical significance should not, however, be confused with practical importance. Just because we can reject a null hypothesis and claim a statistically significant result, does not mean that the result matters. In economics, many data sets are large n, which means it is easy to find statistically significant results that are not of practical importance. Tests of significance have a place in econometrics but are not the be all and end all of inference.
Hypothesis testing can be confusing, but it has a coherent, stable framework that should help you organize the complicated details. The next section demonstrates that there is a sampling distribution for each sample statistic that is a random variable. Section 16.3 will explain how confidence intervals are constructed and interpreted. We then turn to the logic of hypothesis testing (Section 16.4) and explain why the t distribution is so often used (Section 16.5).
- Type
- Chapter
- Information
- Introductory EconometricsUsing Monte Carlo Simulation with Microsoft Excel, pp. 411 - 452Publisher: Cambridge University PressPrint publication year: 2005