Tag Archives: Econometrics

The Problem of Mulitcollinearity

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 x on the dependent variable y. 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

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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.

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Clustered Standard Errors in STATA

In STATA clustered standard errors are obtained by adding the option cluster(variable_name) to your regression, where variable_name specifies the variable that defines the group / cluster in your data. The summary output will return clustered standard errors. Here is the syntax:

 regress x y, cluster(variable_name)

Below you will find a tutorial that demonstrates how to Continue reading Clustered Standard Errors in STATA