Keynesian Economics is a central doctrine in economics that forms the foundation of modern macroeconomic thinking. It was established by John Maynard Keynes during the 1930s. In 1936, the British economist published his book “The General Theory of Employment, Interest and Money” that forms the basis of the Keynesian school. The following series of blog posts will introduce Keynesian Economics, provide useful insights and discuss the most important concepts. The first post is dedicated to aggregate demand, the key concept of Keynesian Economics. The following post explains the dynamics of Keynesian Economics in an aggregate supply/ aggregate demand (AS-AD) model. The third post discusses the remedies of Keynesian Economics in the light of a recession. Finally, the last post elaborates some drawbacks of Keynesianism.Continue reading Keynesian Economics
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In the post that derives the least squares estimator, we make use of the following statement:
This post shows how one can prove this statement. Let’s start from the statement that we want to prove:
How can one save objects in Julia? One easy way to do so it to use the
JLD package. The following examples demonstrates how to save data objects in Julia and how to load the once they are saved.
Julia v0.7 and older
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.
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
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
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 Multicollinearity
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)?
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?