# Cluster Robust Standard Errors in Stargazer

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.

# Robust Standard Errors in Stargazer

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.

# 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.

# Multiple Regression in Julia

Julia presents various ways to carry out multiple regressions. One easy way is to use the lm() function of the GLM package. In this post I will present how to use the lm() and run OLS on the following model

$y = \alpha + \beta_{1} x_{1} + \beta_{2} x_{2} + \beta_{3} x_{3}$

# Linear Regression

A linear regression is a special case of the classical linear regression models that describes the relationship between two variables by fitting a linear equation to observed data. Thereby, one variable is considered to be the explanatory (or independent) variable, and the other variable is considered to be the dependent variable. For instance, an econometrician might want to relate weight to their calorie consumption using a linear regression model.

# 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.

$E(\epsilon_{i}|X)=0$

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