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From |
"Degas Wright" <dwright@cornerstoneadvice.com> |

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

Subject |
RE: st: Dfactor - Optimization Terminated |

Date |
Wed, 20 Oct 2010 16:50:23 -0400 |

Richard, Thank you for your comments. I am new to time series analysis. As it relates to scaling of the data, I have typically standardized data in cross sectional analysis but with time series I did not see the value of the standardization. The _dfactor_ uses the first difference of the data. Is there other scaling approaches that are beneficial in time series analysis. I am concerned with the number of observations (236 weeks of data)- so what is a good rule of thumb related to the number of parameters? Also, your comment on using iterate(#) works fine but I having to stop it a 6 iterations to get it to go through the whole data set. Thank you for your assistance. Degas A. Wright, CFA Chief Investment Officer Decatur Capital Management, Inc. 250 East Ponce De Leon Avenue, Suite 325 Decatur, Georgia 30030 Voice: 404.270.9838 Fax:404.270.9840 Website: www.decaturcapital.com -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Richard Gates Sent: Wednesday, October 20, 2010 4:09 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Dfactor - Optimization Terminated Degas <dwright@cornerstoneadvice.com> is experiencing a non-convergent dynamic factor model using -dfactor-. > Iteration 21: log likelihood = 134.87566 (backed up) > Iteration 22: log likelihood = 134.87566 (backed up) > Iteration 23: log likelihood = 134.87566 (backed up) > optimization terminated because of numerical instability: Hessian is not > negative semidefinite > r(430); As with any optimization problem, convergence is never guaranteed. The example provided by Degas is characteristic of a unidentified problem. When this occurs the optimization search continues indefinitely, or the Hessian becomes numerically singular. An example of the latter is when the estimate of a variance component goes to zero. The strategy here is to put an iteration limit on the search so that it will terminate before the failure occurs, -iterate(20)-, say. The coefficient table will probably reveal the problem. Perhaps the data needs to be scaled. Is there enough data to identify the parameters (7 variance components, 2 AR parameters, and 7 regression coefficients)? Degas states: > I have created a loop that will perform _dfactor_ for each ticker in my > universe of 1000 stocks. ... When running 1000 optimization problems using -dfactor- Degas should expect some non-convergent examples. Perhaps he can utilize the Stata exception handler -capture- with -noisily- to trap any optimization failures and record the failure for further investigation. For example, capture noisily dfactor(D.(r ep mom qer fsr bm np)=,noconstant)(f=,ar(1/2)) /// if xticker==`x', iterate(50) if c(rc) { local failures `failures' `x' continue } Note that I have put a limit of 50 iterations in the code snippet above. The system maximum is 1500 which could cause problems when executing -dfactor- 1000 times in a loop. I have speculated a lot on the source of the trouble. If Degas can provide me with the data for his non-convergent example, I would be happy to investigate the source of the failure. -Richard Gates rgates@stata.com * * 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/ * * 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/

**References**:**Re: st: Dfactor - Optimization Terminated***From:*Richard Gates <rgates@stata.com>

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