Non-linear beta

If you google-finance AMZN you can see the beta is 0.93. I already wrote in the past about this illusive concept. Beta is suppose to reflect the risk of an instrument with respect for example to the market. However, you can estimate this measure in all kind of ways.

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Bias vs. Consistency


Especially for undergraduate students but not just, the concepts of unbiasedness and consistency as well as the relation between these two are tough to get one’s head around. My aim here is to help with this. We start with a short explanation of the two concepts and follow with an illustration.

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Bootstrap Critisim (example)

In a previous post I underlined an inherent feature of the non-parametric Bootstrap, it’s heavy reliance on the (single) realization of the data. This feature is not a bad one per se, we just need to be aware of the limitations. From comments made on the other post regarding this, I gathered that a more concrete example can help push this point across.

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Detecting bubbles in real time

Recently, we hear a lot about a housing bubble forming in UK. Would be great if we would have a formal test for identifying a bubble evolving in real time, I am not familiar with any such test. However, we can still do something in order to help us gauge if what we are seeing is indeed a bubbly process, which is bound to end badly.

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R vs MATLAB – round 2

R takes it. I prefer coding in R over MATLAB. I feel R understands that I do not like to type too much. A few examples:

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Comments on Comments in R

When you are busy with a lengthy project, like writing a paper, you create many objects along the way. Every time you log into the project, you need to remember what is what. In the past, each new working session I used to rerun the script anew and follow what each line is doing until I get back the objects I need and continue working. Apart from helping you remember what you are doing, it is very useful for reproducibility, at least given your data, in the sense that you are sure nothing is overrun using the console and it is all there. Those days are over.

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Omitted Variable Bias

Frequently, we see the term ‘control variables’. The researcher introduces dozens of explanatory variables she has no interest in. This is done in order to avoid the so-called ‘Omitted Variable Bias’.

What is Omitted Variable Bias?

In general, OLS estimator has great properties, not the least important is the fact that for a finite number of observations you can faithfully retrieve the marginal effect of X on Y, that is E(\widehat{\beta}) = \beta. This is very much not the case when you have a variable that should be included in the model but is left out. As in my previous posts about Multicollinearity and heteroskedasticity, I only try to provide the intuition since you are probably familiar with the result itself.

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