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 Multicollinearity

# Tag Archives: Econometrics

# 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.

# 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

# Julia-R Cheatsheet – Mathematical Operations

# Mathematical Operations

What are the commands for the most important mathematical operations in Julia and R? The following table translates the most common Julia commands into R language.

Continue reading Julia-R Cheatsheet – Mathematical Operations

# Julia-R Cheatsheet – Accessing Vector/Matrix Elements

# Accessing Vector/Matrix Elements

How to access vector and matrix elements in Julia and R? The following table translates the most common Julia commands into R language.

Continue reading Julia-R Cheatsheet – Accessing Vector/Matrix Elements

# Julia-R Cheatsheet – Manipulating Vectors and Matrices

# Manipulating Vectors and Matrices

How to manipulate vectors and matrices in Julia and R? The following table translates the most common Julia commands into R language.

Continue reading Julia-R Cheatsheet – Manipulating Vectors and Matrices

# Julia-R Cheatsheet – Creating Random Numbers

# Creating Random Numbers

How to create random number in Julia and R? The following table translates the most common Julia commands into R language.

Continue reading Julia-R Cheatsheet – Creating Random Numbers

# Julia-R Cheatsheet – Creating Matrices

# Creating Matrices

How to create matrices in Julia and R? The following table translates the most common Julia commands into R language.

# Julia-R Cheatsheet – Creating Vectors

# Creating Vectors

How to create vectors in Julia and R? The following table translates the most common Julia commands into R language.

# Seasonal Adjustment in R

The package ‘Seasonal’ facilitates seasonal adjustment in R. The R package provides an easy-to-handle wrapper around the X-13ARIMA-SEATS Fortran libraries provided by the US Census Bureau. X-13ARIMA-SEATS is the state-of-the-art seasonal adjustment software produced, distributed, and maintained by the Census Bureau. Continue reading Seasonal Adjustment in R