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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: SE with cluster option |

Date |
Tue, 18 Oct 2005 18:35:55 +0000 |

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

>

> _________________________________________________________________

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>

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

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**Follow-Ups**:**Re: st: SE with cluster option***From:*"Mark Schaffer" <M.E.Schaffer@hw.ac.uk>

**References**:**Re: st: SE with cluster option***From:*"Mark Schaffer" <M.E.Schaffer@hw.ac.uk>

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