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
18 - Omitted Variable Bias
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
… if the more educated workers tend to be more intelligent, motivated, or blessed with advantageous family backgrounds, … then the more educated workers typically would have received higher wages even without their additional schooling. It therefore is difficult to ascertain how much of the empirical association between wages and schooling is due to the causal effect of schooling and how much is due to unobserved factors that influence both wages and schooling.
John Bound and Gary SolonIntroduction
In this chapter we discuss the consequences of not including an independent variable that actually does belong in the model. We revisit our discussion in Chapter 13 about the role of the error term in the classical econometric model. There we argue that the error term typically accounts for, among other things, the influence of omitted variables on the dependent variable. The term omitted variable refers to any variable not included as an independent variable in the regression that might influence the dependent variable. In Chapter 13 we point out that, so long as the omitted variables are uncorrelated with the included independent variables, OLS regression will produce unbiased estimates. In this chapter we focus on the issue of omitted variables and highlight the very real danger that omitted variables are in fact correlated with the included independent variables. When that happens, OLS regression generally produces biased and inconsistent estimates, which accounts for the name omitted variable bias.
- Type
- Chapter
- Information
- Introductory EconometricsUsing Monte Carlo Simulation with Microsoft Excel, pp. 490 - 507Publisher: Cambridge University PressPrint publication year: 2005
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