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From |
John Hendrickx <john_hendrickx@yahoo.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: multicollinearity test for probit? |

Date |
Thu, 11 Dec 2003 10:10:31 -0800 (PST) |

--- "Matt A. Barreto" <Mbarreto@uci.edu> wrote: > Is there a similar command to vif following regress when using > probit or > oprobit (or logit/ologit) to test for multicollinearity among > independent > variables in a probit equation? > Others have noted that collinearity is a problem among righthand side variables and the vif and condition diagnostics from a regression model are valid for a probit model with those independent variables. I've done some work on an alternative approach using SPSS and recently I've been working on a Stata version. The basic problem at the time was that collinearity diagnostics aren't useful if there are interactions or polynomial terms in the model, according to Belsley, D.A. (1991). "Conditioning diagnostics, collinearity and weak data in regression", New York: John Wiley & Sons. The basic problem with collinearity is that because of the strong correlation among independent variables, a small change in the value of one of them can result in very different coefficients for other collinear variables. (This is discernable in the large standard errors of coefficients where collinearity is strong). In models with independent variables in main effects and interactions, and in linear, squared etc terms, the usual diagnostics don't take these relationships between model terms into account. This was Belsley's main argument against the use of collinearity diagnostics for interactions as I recall. He also wasn't satisfied with the VIF statistic and showed that it can't always detect multicollinearity. Condition indices are better but even if they indicate multicollinearity, the problem need not be serious if the dataset is large enough to provide stable estimates. Ok, the solution gleaned from Besley and implemented by a group of us was to add a small random value to selected independent variables and re-estimate the model. Iterate 100 or so times, and assess the stability of the estimates. An SPSS macro for doing this is at http://www.xs4all.nl/~jhckx/spss/perturb/perturb.html along with a little more background information. Here's a Stata do-file for performing the same procedure. It should be fairly simple to adapt it to other problems, just modify the lines with prtb* variables and change the macro if there are other transformations of independent variables. It should work with probit or any other procecure that produces an e(b) matrix of coefficients. As said, I'm working on a proper Stata ado file. Comments on this procedure are very welcome. HTH, John Hendrickx ------------------------------------- set memory 16m set matsize 150 use recoded xi3 ses fses*eyr educyr*eyr fses*exp educyr*exp exp2 corr fses eyr _IfsXey educyr _IedXey exp _IfsXex _IedXex exp2 collin fses eyr _IfsXey educyr _IedXey exp _IfsXex _IedXex exp2 xi3: regr ses fses*eyr educyr*eyr fses*exp educyr*exp exp2 vif matrix allb=e(b) gen prtb1 = 0 gen prtb2 = 0 gen nlin1 = 0 label var prtb1 "eyr + uniform(-2.5,2.5)" label var prtb2 "exp + uniform(-2.5,2.5)" label var nlin1 "exp^2" program define exp2 quietly replace nlin1=prtb2^2 end forval i=1/100 { quietly replace prtb1=eyr+(uniform()-.5)*5 quietly replace prtb2=exp+(uniform()-.5)*5 exp2 quietly xi3: regr ses fses*prtb1 educyr*prtb1 fses*prtb2 educyr*prtb2 nlin1 matrix allb=allb\e(b) } matrix list allb drop _all svmat allb, names(eqcol) summarize __________________________________ Do you Yahoo!? New Yahoo! Photos - easier uploading and sharing. http://photos.yahoo.com/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: multicollinearity test for probit?***From:*"Matt A. Barreto" <Mbarreto@uci.edu>

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