Omitted variables bias (or sometimes omitted variable bias) is a standard expression for the bias that appears in an estimate of a parameter if the regression run does not have the appropriate form and data for other parameters. For example, many regressions that have wage or income as the dependent variable suffer from omitted variables bias because there is often no practical way to add in a worker's innate ability or motivation as an explanatory variable. As a result, the estimated coefficients on variables such as education as likely to be biased because of the correlation between educational attainment and unobserved ability. If the correlation between education and unobserved ability is positive, omitted variables bias will occur in an upward direction. Conversely, if the correlation between an explanatory variable and an unobserved relevant variable is negative, omitted variables bias will occur in a downward direction.

Definition and Use of Instrumental Variables in Econometrics

Linear Regression Analysis

The Differences Between Explanatory and Response Variables

Correlation Analysis in Research

What You Should Know About Econometrics

What Are Residuals?

Structural Equation Modeling

What It Means When a Variable Is Spurious

What Is a Scatterplot?

How Intervening Variables Work in Sociology

Paired Data in Statistics

What Is a Least Squares Line?

The Significance of Negative Slope

The Slope of the Regression Line and the Correlation Coefficient

The Importance of Exclusion Restrictions in Instrumental Variables

Your Comprehensive Guide to a Painless Undergrad Econometrics Project