Omitted Variable Bias

Omitted Variable Bias (OVB) occurs when a relevant variable is excluded from a regression model. This can lead to biased and inconsistent estimates of the coefficients of the included variables, particularly when the omitted variable is correlated with the included variables.

1. Understanding OVB:

• Occurs when an important variable is left out of the model

• Leads to biased estimates of included variables

• Particularly problematic when omitted variable is correlated with included variables

• Can lead to incorrect causal interpretations

2. Direction of Bias:

• Bias depends on the correlation between included and omitted variables

• Also depends on the effect of the omitted variable on the dependent variable

• Can be positive or negative

• Magnitude of bias increases with stronger correlations

3. Solutions and Prevention:

• Include all relevant variables

• Use instrumental variables

• Consider fixed effects

• Collect additional data

The following simulation allows you to explore how omitted variables affect coefficient estimates and how the bias depends on the correlation between included and omitted variables: