Few weeks back I gave a talk in the R/Finance 2016 conference, about forecast combinations in R. Here are the slides:
Package Eplot on cran.
At least for me, R by faR. MATLAB has its own way of doing things, which to be honest can probably be defended from many angles. Here are few examples for not so subtle differences between R and MATLAB:
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.
Matlab has it this time, with solid 3D plotting capabilities.
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.
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.
If you have 10 possible independent regressors, and none of which matter, you have a good chance to find at least one is important.
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.
A vector autoregression (VAR) process can be represented in a couple of ways. The usual form is as follows:
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.
Albert Schweitzer said: “Example is not the main thing in influencing others. It is the only thing.”, so I start with it.
Open CPU is a great project. Few months back, I wrote a function for plotting a moving window of the market average correlation. Jeroen C.L. Ooms was nice enough to upload it to their server. Something is now changed. Quotes now return as a character class, as oppose to numeric. This messes up the function and the plot does not renders. I don’t wish to disturb Jeroen C.L. Ooms again with the correction for the code (despite his kind replies in the past). This problem creates the opportunity to look at the glistening “Shiny” package. I used it to (quickly..) build an app for the plot. You can now view a live correlation plot with the moving window of your choice. Live, as the app requests current market data. The width of the window for correlation calculation is given as an input parameter.