Wednesday, June 20, 2012

api for plots

In regard to the code for the plots, the function is:

 mv_mean_contour(self, mu1_l, mu1_u, mu2_l, mu2_u, step1, step2,
                        levs=[.2, .1, .05, .01, .001], plot_dta=False)

The user enters the mean ranges of both variables and the interval in which to conduct a joint hypothesis test of the means.  Before, these had to be entered by the user because there were problems with hypothesis tests with very large likelihood ratios.  however, I think I fixed this problem and can now make all of the input parameters optional.  It will take some fiddling first. 

Sunday, June 10, 2012


I knew that coming into this project, being familiar with numerical optimization methods would be important.  As the "beginning" period has ended and the "stretch" has begun, I am happy to say that I already learned the theory and implementation of Newton's method for optimization.  With the exception of simulated annealing, this is the only optimization method I feel comfortable enough with the theory to be able to code it myself and know when it is appropriate and when it isn't.  Constant learning is without question the top benefit of participating in GSOC.

On another note, I had some fun with empirical likelihood confidence regions.  Below is a plot of the confidence regions for the mean daily percent return for the Google and Microsoft stock from June 10, 2011 to June 10, 2012.

and here is the same thing but for only the last 30 trading days and the actual returns plotted.