The eve of Lehman Brothers' demise Recently Moody’s warned the US government to degrade its credit rating if the nation’s debt limit increase is not approved [Ref. 1]. The news came right after Standard & Poor’s lowered US’s sovereign rating from AAA to AA. Those rating changes suggest the accumulation of default risk and may cause some butterfly effect. Before the start of this Great Recession, without much notice, Lehman Brothers’ default probability increased drastically according to a classic model by Merton [Ref. 2]. And this change failed to be disclosed by either Standard & Poor’s rating or the stock price. Here I translated Gunter and ...
Bootstrap prediction models for probability of default Not like consumer credit scoring, corporate default study is usually jeopardized by the low-n-low-p data sizes. In the fourth chapter of their book, Gunter and Peter, demonstrated an example about how to construct prediction models for IDR (invesment grade default rate) using VBA and therefore evaluate them by residual sum of squares [Ref. 1]. The only shortcoming is that the variables are limited and the observations are scarce (25 total, 22 valid), which makes me feel awkward to estimate the distribution. In this case, bootstrapping may be a good alternative, since it is a simple and straightforward method to increase ...
A two-step transpose approach to reshape data New in SAS 9.2, the TRANSPOSE procedure accepts multiple IDs in its ID statement. More than one IDs would automatically concatenate together as the new variable names. Previously, Proc Transpose usually only allows one ID. As the result, the concatenation of variable names has to be done by DATA step array in SAS 9.1 or earlier versions. This change would bring more flexibility to reshape data to any desired structure. In this case, a small file with date, gender and 3 credit records is transformed to a more flat data structure, only corresponding to the date. Gender would be moved ...
SAS and Revolution R for GB size data R surpassed Matlab and SAS on recent Tiobe Programming index for popularity. I agree with this ranking[1], since R could largely replace Matlab and SAS for MB size data, and it is getting hot. The fast growing sectors, quantitative finance, bioinformatics and web analytics, are all embracing R. In those competitive fields, survival demands the skills of a developer instead of a programmer, which suggests something brings more power or higher AUC. The cross-platform, open-sourced, C-based, function-friendly R is right on top of the tide. SAS programmers have to wait SAS Institute to build tools for them, while an R ...
Find the 'right' SAS functions How many functions SAS has? Well, it sounds like a job interview question. For SAS 9.2, by querying the system dictionary (sashelp.vfunc or dictionary.functions), the exact answer is 946, including all functions and call routines. There are two types - unicode/bit based on input argument, while three types –numeric/character/bitwise based on output argument. Again according to their usage[1], the common SAS functionscan be categorized into several types: array(3), bitwise logical operation(3), PERL regular expression(11), character(91), time(38), descriptive statistics(32), random number(22), probability(18) , mathematics(36), finance(32), etc. Some functions have evolved for several generations Since SAS has development history of more than ...