Multicollinearity is a common problem in econometrics. As explained in a previous post, multicollinearity arises when we have too few observations to precisely estimate the effects of two or more highly correlated variables on the dependent variable. This post tries to graphically illustrate the problem of multicollinearity using venn-diagrams. The venn-diagrams below all represent the following regression model Continue reading Graphically Illustrate Multicollinearity: Venn Diagram

# Tag Archives: clrm

# Omitted Variable Bias: An Example

This post is part of the series on the omitted variable bias and provides a simulation exercise that illustrates how omitting a relevant variable from your regression model biases the coefficients. The R code will be provided at the end. Continue reading Omitted Variable Bias: An Example

# Omitted Variable Bias

The omitted variable bias is a common and serious problem in regression analysis. Generally, the problem arises if one does not consider all relevant variables in a regression. In this case, one violates the third assumption of the assumption of the classical linear regression model. The following series of blog posts explains the omitted variable bias and discusses its consequences.

# Omitted Variable Bias: What can we do about it?

To deal with an omitted variables bias is not easy. However, one can try several things.

First, one can try, Continue reading Omitted Variable Bias: What can we do about it?

# Omitted Variable Bias: Violation of CLRM–Assumption 3: Explanatory Variables must be exogenous

One reason why the omitted variable leads to biased estimates is that omitting a relevant variable violates assumption 3 of the necessary assumptions of the classical regression model that states that all explanatory variables must be exogenous, i.e.

From this post, we know that omitting a relevant variable from the regression causes the error term and the explanatory variables to be correlated.

Continue reading Omitted Variable Bias: Violation of CLRM–Assumption 3: Explanatory Variables must be exogenous

# Omitted Variable Bias: Conclusion

The following post provides a recap of the previous posts on the omitted variable bias (Introduction, Explanation, In-depth discussion of the bias, Consequences of the omitted variable bias) and concludes with some general advise. In case you haven’t read the previous posts, you might want to start from the beginning in order to fully understand the issues related to the omitted variable bias.

All in all, the omitted variable bias is a severe problem. Neglecting a relevant variable leads to biased and inconsistent estimates. Hence, as a general advice, when you are working with linear regression models, you should pay close attention to potentially omitted variables. In particular, you should ask yourself the following questions: Continue reading Omitted Variable Bias: Conclusion

# Omitted Variable Bias: Consequences

In this post, we will discuss the consequence of the omitted variable bias in a more elaborate way. Particularly, we will show that omitting a variable form the regression model violates an OLS assumption and discuss what will happen if this assumption is violated.