Re: st: SE with cluster option

["Alexander Nervedi" <alexnerdy@hotmail.com>]

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
> email: m.e.schaffer@hw.ac.uk
> web: http://www.sml.hw.ac.uk/ecomes
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