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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: RE: Methodology question |

Date |
Wed, 16 Jun 2010 22:40:59 +0100 |

The implied reference is Harrell, F. 2001. Regression modeling strategies. New York: Springer. I also have a more general comment. Postings of the forms "I have this plan. What comments do you have?" "What should I do be doing in my project?" "I am doing this. Is this correct?" are unlikely to get much response. That is a statement of probability and in no sense implies a ban or rules out your good fortune that someone may have something interesting or useful to say. In practice, it is difficult to say what does and does not make sense for anybody else to do. We do not know the context, or the data, or at what level the person is operating. Also, it is tricky often: Something may look like someone's project for a course or degree. Shouldn't the students be working that out themselves, not pleading support on the net? The most successful threads on this list, at least in my experience, grow out of Stata-specific questions, which sometimes morph into interesting questions of statistical principles or practice. The other way round doesn't work so well. Again, I deal in probabilities here, not certainties. What else would you expect on a statistical list? Nick n.j.cox@durham.ac.uk Lachenbruch, Peter This procedure is essentially a stepwise regression and has all the headaches associated with this procedure. The book by Frank Harrell in Springer series (about 2002) discusses this. You might also investigate the lars command, but you need to read up on variable selection methods a bit. Prasad Ramani I am new here and my question is quite different from what is normally asked here. I have a few questions more from an application point of view. The Project =========== I am analyzing a multi asset class portfolio whose composition has changed over the years from mainly equities to a mix of equities, fixed income, hedge funds & private equity. The objective of the analysis is to find which risk factors the portfolio is exposed to and how to hedge them. The data is a monthly series of returns of this portfolio for the past 7-8 years. My Proposed Methodology ==================== 1. Get monthly returns for a list of indices that represent the major asset classes: For equities: SP500, MSCI World etc., for Fixed Income: BarCap US Aggregate Bond fund, JP Morgan Emerging Market Bond index, for Commodities: Gold, Oil, for Interest rates: 3 month LIBOR etc. I end up with about 15 such factors...Factor 1 to Factor 15. 2. I come up with a correlation structure for these 15 factors based on weekly/monthly returns going back to about 3 years. 3. I regress the returns of my portfolio against these 15 factors...and based on the t-stats of the factors and the overall adj r-squared, I eliminate those factors that are insignificant at 5% level. 4. I expect the ones with low t-stats to be highly correlated to some other factors...and this can be verified from the above Var-Covar matrix (point 2) Finally I end up with those factors that have significant t-stats, F-stat and adj r-squared. I would really appreciate if you can give me your views on this. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: RE: Methodology question***From:*P Ram <analyst.q@gmail.com>

**References**:**st: Methodology question***From:*Prasad Ramani <analyst.q@gmail.com>

**st: RE: Methodology question***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

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