In Julia, you can set a seed to the random number generator using the `srand()`

function. The code example below sets the seed to 1234. Generating a random variable with `rand(1)`

after setting the seed to 1234 will always generate the same number, i.e. it will always return 0.5908446386657102. Continue reading How to set a Seed in Julia?

# Monthly Archives: August 2018

# Upgrade Debian 8 to 9

In this post I am going to explain how to upgrade Debian 8 to Debian 9. You can enter the following eight steps in your terminal: Continue reading Upgrade Debian 8 to 9

# Graphically Illustrate Multicollinearity: Venn Diagram

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

# 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

# The Problem of Multicollinearity

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

# What is Total Factor Productivity (TFP)?

In growth theory, changes in output (GDP) are explained through changes of production factors, i.e. changes in labour or capital. Economists consider the residual, i.e. the part of changes in output that one cannot explain with changes of production factors, as total factor productivity (TFP) or technological change. In contrast to labour productivity, that relates output only to labour, total factor productivity states how efficiently an economy uses all its production factors. Continue reading What is Total Factor Productivity (TFP)?

# What is Labour Productivity?

Newspapers, and in particular economists frequently talk about labour productivity. Especially when commenting the current state of the economy, labour productivity is of central concern. This post will explain the concept of labour productivity and highlight its importance in the context of business cycle analysis. Continue reading What is Labour Productivity?