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.
Category: Blog
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.
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.
Bootstrap criticism
The title reads Bootstrap criticism, but in fact it should be Non-parametric bootstrap criticism. I am all in favour of Bootstrapping, but I point here to a major drawback.
My favourite statistician
We are all standing on the shoulders of giants. Bradley Efron is one such giant. With the invention of the bootstrap in 1979 and later with his very influential 2004 paper about the Least Angle Regression (and the accompanied software written in R).
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:
R vs Matlab (round 1)
Matlab has it this time, with solid 3D plotting capabilities.
Don’t believe anything you read
I just finished reading An estimate of the science-wise false discovery rate and application to the top medical literature. The authors ask how many of what we read is scientific journals is actually incorrect, or false.
Most popular posts – 2013
Here (what people think) are the most interesting posts in 2013:
Understanding Multicollinearity
On p-value
Bootstrapping time series
Quantile Autoregression in R
My Own favourite:
How Important is Variable Selection?
What is overfitting?
Overfitting is strongly related to variable selection. It is a common problem and a tough one, best explained by way of example.
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.
The Importance of being inauthentic
Presenting properly is important. Here is how I think it should look like,
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 
. 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.
Bayesian vs. Frequentist in Practice (cont’d)
Few weeks back I simulated a model and made the point that in practice, the difference between Bayesian and Frequentist is not large. Here I apply the code to some real data; a model for Industrial Production (IP).
Stocks with upside potential
THIS IS NOT INVESTMENT ADVICE. ACTING BASED ON THIS POST MAY, AND IN ALL PROBABILITY WILL, CAUSE MONETARY LOSS.
Quantile regression is now established as an important econometric tool. Unlike mean regression (OLS), the target is not the mean given x but some quantile given x. You can use it to find stocks that present good upside potential. You may think it has to do with the beta of a stock, but the beta is OLS-related, and is symmetric. High-beta stock rewards with an upside swing if the market spikes but symmetrically, you can suffer a large draw-down when the market drops. This is not an upside potential.