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 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

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)?


“In God we trust; all others must bring data.” W. Edwards Deming