Even though this list has been around for quite a while (I think inesad.edu.bo posted it first in autumn 2012), I think it just doesn’t get old. On the contrary each time I read through it, I just can’t stop laughing. Hope you enjoy this little re-post 🙂
Continue reading 21 Reasons Why You Should Never Date an Economist
R presents various ways to carry out linear regressions. The most natural way is to use the lm() function, the R build-in OLS estimator. In this post I will present you how to use lm() and run OLS on the following model
This post shows how to manually construct the OLS estimator in R (see this post for the exact mathematical derivation of the OLS estimator). In contrary to a previous post, this post focuses on setting up the OLS estimator as a R function. While the aim of the former post was much more on the construction of the OLS estimator in general, is this post all about constructing a functional form around the estimator. Continue reading Construct the OLS estimator as a function in R
This post shows how to manually construct the OLS estimator in R (see this post for the exact mathematical derivation of the OLS estimator). The code will go through each single step of the calculation and estimate the coefficients, standard errors and p-values. In case you are interested the coding an OLS function rather than in the step wise calculation of the estimation itself I recommend you to have a look at this post. Continue reading Calculate OLS estimator manually in R
Assumption 5 is often listed as a Gauss-Markov assumption and refers to normally distributed error terms 
Violating assumption 4.1 of the OLS assumptions, i.e. 
Assumption 4 of the four assumption required by the Gauss-Markov theorem states that the error terms of the population 
From a previous posts on the Gauss Markov Theorem and OLS we know that the assumption of unbiasedness must full fill the following condition Continue reading Proof Gauss Markov Theorem
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