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
Multicollinearity or collinearity refers to a situation where two or more variables of a regression model are highly correlated. Because of the high correlation, it is difficult to disentangle the pure effect of one single explanatory variables on the dependent variable . From a mathematical point of view, multicollinearity only becomes an issue when we face perfect multicollinearity. That is, when we have identical variables in our regression model. Continue reading The Problem of Mulitcollinearity
In a previous post, we discussed how to obtain clustered standard errors in R. While the previous post described how one can easily calculate cluster robust standard errors in R, this post shows how one can include cluster robust standard errors in stargazer and create nice tables including clustered standard errors.
In a previous post, we discussed how to obtain robust standard errors in R. While the previous post described how one can easily calculate robust standard errors in R, this post shows how one can include robust standard errors in stargazer and create nice tables including robust standard errors.
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 blog posts explain the omitted variable bias and demonstrate its consequences.
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?