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From |
"Mark Schaffer" <M.E.Schaffer@hw.ac.uk> |

To |
"Alexander Nervedi" <alexnerdy@hotmail.com> |

Subject |
Re: st: SE with cluster option |

Date |
Tue, 18 Oct 2005 20:08:56 +0100 (BST) |

Al, Other patterns in the data can generate this problem. For example, you might have a variable that is the cluster equivalent of a singleton dummy: the variable has two values, =x for all obs in one cluster and =y for the rest. It's ad-hocky, but try running your regression clustering on household (survey code) but dropping one variable at a time and seeing if and when the problem goes away. This will let you trace the problem. Cheers, Mark NB: Does the above recommendation remind anyone else of the following ancient computing joke? Q: How do you know that's an IBM repairman on the side of the road with a flat tire? A: He changes each tire, one after the other, until he finds out which one is flat. > Hi Mark, > > Yes! I clicked that and it goes on to talk about situations in which > F(.,.) > goes missing. All the discussion is about when the number of parameters is > equal to or more than the number of observations. For example, "You might > see chi2(6) or F(6, 5). If you were to count the number of coefficients > that would be constrained to 0 in a model test in this case, you would > find > that number to be greater than 6. You could find out what that number is > by > reestimating the model parameters without the robust and cluster() > options". > > I dont think this is my problem - I have enough observations (about 40 > observations per cluster per season (so about 120 since i have three > seasons)). Also I can estimate the model with robust, but not with > cluster(). > > So i am not sure what is going on. > > Thanks for your email Mark! > > -Anerdy > > > >>From: "Mark Schaffer" <M.E.Schaffer@hw.ac.uk> >>Reply-To: statalist@hsphsun2.harvard.edu >>To: statalist@hsphsun2.harvard.edu >>CC: "mes " <m.e.schaffer@hw.ac.uk> >>Subject: Re: st: SE with cluster option >>Date: Tue, 18 Oct 2005 19:17:49 +0100 (BST) >> >>Al, >> >> > Hi Everyone, >> > >> > I was wondering what may explain the following F(.,.) valuse when i >> use >> > the cluster option. I have about 40 households per cluister, and four >> > clusters (total of 168 unique households). I'd like to run the model >> at >> > the cluster level to estimate a Difference in Difference model. >> > >> > Initially I thought the issue was that since there are only 4 >> clusters, >> > I'd not be able to estimate it since its using 4 cluster means to >>estimate >> > the standard errors. >> >>You are right - in effect, you have 4 observations ("super-observations" >>is perhaps more accurate) to calculate your var-cov matrix, which means >>you won't get very far this way. >> >> > However the problem still remains if i cluster at the >> > survey code (or household) level >> >>Is there a clickable hyperlink on the missing F-stat in this case, and if >>so, what does it say? >> >>--Mark >> >> >> > -MODEL 1 - >> > >> > reg y1 DiD vdc post season cdum2 cdum4, cluster(clust) >> > >> > Regression with robust standard errors Number of obs = >> > 672 >> > F( >> > 1, >> > 3) = . >> > >>Prob >> > > >> > F = . >> > >> > R-squared = 0.1220 >> > Number of clusters (village) = 4 Root MSE >>= >> > .29762 >> > >> > >>------------------------------------------------------------------------------ >> > | Robust >> > cropfail | Coef. Std. Err. t P>|t| [95% Conf. >> > Interval] >> > >>-------------+---------------------------------------------------------------- >> > DiD | .1867678 .0381533 4.90 0.016 .0653468 >> > .3081888 >> > cdum1 | .0407624 .0190767 2.14 0.122 -.0199481 >> > .1014729 >> > post | .0377531 .0255782 1.48 0.236 -.0436482 >> > .1191544 >> > season | -.0803571 .0418741 -1.92 0.151 -.2136192 >> > .0529049 >> > cdum2 | .0830587 5.54e-16 . 0.000 .0830587 >> > .0830587 >> > cdum4 | .085874 1.02e-15 . 0.000 .085874 >> > .085874 >> > _cons | .1601304 .0901628 1.78 0.174 -.1268078 >> > .4470686 >> > >>------------------------------------------------------------------------------ >> > >> > >> > -MODEL 2 - >> > >> > reg y1 DiD vdc post season vdum2 vdum4, cluster(survey) >> > Regression with robust standard errors Number of obs = >> > 672 >> > F( >> > 5, >> > 167) = . >> > >>Prob >> > > >> > F = . >> > >> > R-squared = 0.1220 >> > Number of clusters (survey) = 168 Root MSE = >> > .29762 >> > >> > >>------------------------------------------------------------------------------ >> > | Robust >> > cropfail | Coef. Std. Err. t P>|t| [95% Conf. >> > Interval] >> > >>-------------+---------------------------------------------------------------- >> > DiD | .1867678 .0788515 2.37 0.019 .0310936 >> > .342442 >> > cdum1 | .0407624 .012909 3.16 0.002 .0152765 >>.0662484 >> > post | .0377531 .0240521 1.57 0.118 -.0097322 >> > .0852384 >> > season | -.0803571 .0200387 -4.01 0.000 -.119919 >> > -.0407952 >> > cdum2 | .0830587 .0201067 4.13 0.000 .0433627 >> > .1227547 >> > cdum4 | .085874 .0476556 1.80 0.073 -.008211 >> > .179959 >> > _cons | .1601304 .0483279 3.31 0.001 .0647181 >> > .2555428 >> > >>------------------------------------------------------------------------------ >> > >> > >> > -MODEL 3 - >> > . reg y1 DiD vdc post season vdum2 vdum4, robust >> > >> > Regression with robust standard errors Number of obs = >> > 672 >> > F( 6, 665) = >> > 10.49 >> > Prob > F = >> > 0.0000 >> > R-squared = >> > 0.1220 >> > Root MSE = >> > .29762 >> > >> > >>------------------------------------------------------------------------------ >> > | Robust >> > cropfail | Coef. Std. Err. t P>|t| [95% Conf. >> > Interval] >> > >>-------------+---------------------------------------------------------------- >> > DiD | .1867678 .0658962 2.83 0.005 .0573781 >> > .3161575 >> > cdum1 | .0407624 .0144458 2.82 0.005 .0123976 >> > .0691272 >> > post | .0377531 .0276749 1.36 0.173 -.0165876 >> > .0920938 >> > season | -.0803571 .0229621 -3.50 0.000 -.1254441 >> > -.0352702 >> > cdum2 | .0830587 .0206597 4.02 0.000 .0424926 >> > .1236247 >> > cdum4 | .085874 .0436286 1.97 0.049 .0002076 >> > .1715403 >> > _cons | .1601304 .0566039 2.83 0.005 .0489866 >> > .2712742 >> > >>------------------------------------------------------------------------------ >> > >> > >> > Model 1 estimates the SEs at the cluster level, while Model 2 does it >> at >> > the >> > ID level. Model 3 uses the robust option. and everything works out >> fine. >> > The >> > help suggests that I may be estimating more parameters than i can >>possible >> > estimate with the data. I am not sure i see that since i have a sample >>of >> > over 670 observations, and I am estimating betwen 5 - 8 variable at >>most. >> > >> > I was hoping someone has some intuition here as to what may be messing >>me >> > up. >> > >> > thanks. >> > al >> > >> > _________________________________________________________________ >> > Express yourself instantly with MSN Messenger! Download today - it's >>FREE! >> > http://messenger.msn.click-url.com/go/onm00200471ave/direct/01/ >> > >> > * >> > * 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/ >> > >> >> >>Prof. Mark Schaffer >>Director, CERT >>Department of Economics >>School of Management & Languages >>Heriot-Watt University, Edinburgh EH14 4AS >>tel +44-131-451-3494 / fax +44-131-451-3294 >>email: m.e.schaffer@hw.ac.uk >>web: http://www.sml.hw.ac.uk/ecomes >> > > _________________________________________________________________ > Don’t just search. Find. Check out the new MSN Search! > http://search.msn.click-url.com/go/onm00200636ave/direct/01/ > > Prof. Mark Schaffer Director, CERT Department of Economics School of Management & Languages Heriot-Watt University, Edinburgh EH14 4AS tel +44-131-451-3494 / fax +44-131-451-3294 email: m.e.schaffer@hw.ac.uk web: http://www.sml.hw.ac.uk/ecomes __________________________________________________________________ DISCLAIMER: This e-mail message is subject to http://www.hw.ac.uk/disclaim.htm __________________________________________________________________ * * 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**:**Re: st: SE with cluster option***From:*"Mark Schaffer" <M.E.Schaffer@hw.ac.uk>

**Re: st: SE with cluster option***From:*"Alexander Nervedi" <alexnerdy@hotmail.com>

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