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.
Bayesian vs. Frequentist in Practice
Rivers of ink have been spilled over the ‘Bayesian vs. Frequentist’ dispute. Most of us were trained as Frequentists. Probably because the computational power needed for Bayesian analysis was not around when the syllabus of your statistical/econometric courses was formed. In this age of tablets and fast internet connection, your training does not matter much, you can easily transform between the two approaches, engaging the right webpages/communities. I will not talk about the ideological differences between the two, or which approach is more appealing and why. Larry Wasserman already gave an excellent review.
Understanding Multicollinearity
Roughly speaking, Multicollinearity occurs when two or more regressors are highly correlated. As with heteroskedasticity, students often know what does it mean, how to detect it and are taught how to cope with it, but not why is it so. From Wikipedia: “In this situation (Multicollinearity) the coefficient estimates may change erratically in response to small changes in the model or the data.” The Wikipedia entry continues to discuss detection, implications and remedies. Here I try to provide the intuition.
How Important is Variable Selection?
Very.
If you have 10 possible independent regressors, and none of which matter, you have a good chance to find at least one is important.
Design quotes
Slides 18 and 30 are especially nice:
Quantify your jogging
Numbers are useful (I think we can all agree on that..). If you own a smart phone, you can install this runmeter app. When you run, you can take the smartphone with you and activate this app to collect interesting numbers like distance, pace, fastest pace, heart rate*, calories etc. Now we can load the statistics collected over the past months into R and have a quantified look at the progress.
R and Dropbox
When you woRk, you probably have a set of useful functions/packages you constantly use. For example, I often use the excellent quantmod package, and the nice multi.sapply function. You want your tools loaded when R session fires.
Moving Average Representation of VAR
A vector autoregression (VAR) process can be represented in a couple of ways. The usual form is as follows:
Multivariate summary function in R
Some time ago, I wrote a Better summary function in R . Here is its multivariate extension:
Quantile Autoregression in R
In the past, I wrote about robust regression. This is an important tool which handles outliers in the data. Roger Koenker is a substantial contributor in this area. His website is full of useful information and code so visit when you have time for it. The paper which drew my attention is “Quantile Autoregression” found under his research tab, it is a significant extension to the time series domain. Here you will find short demonstration for stuff you can do with quantile autoregression in R.
On p-value
Albert Schweitzer said: “Example is not the main thing in influencing others. It is the only thing.”, so I start with it.
Heteroskedasticity tests
Assume you have a variable y, which has an expectation and a variance. The expectation is often modeled using linear regression so that E(y) equals, on average, $\beta_0 +\beta_1x$. The origin of the variability in y is the residual. Now, standard econometric courses start with the simple notion of “constant variance”, which means that the variance of the disturbances is steady and is not related to any of the explanatory variables that were chosen to model the expectation, this is called homoskedasticity assumption. In fact, in real life it is rarely the case. Courses should start with the heteroskedasticity assumption as this is the prevalent state of the world. In almost any situation you will encounter, the variance of the dependent variable is not constant, it matters what is the x for which we want to determine the variance of y.