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Re: st: Matsize Increase Leads to Zero Model Degrees of Freedom


From   David Muller <davidmull@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Matsize Increase Leads to Zero Model Degrees of Freedom
Date   Mon, 2 Sep 2013 09:59:49 +0200

Hi Alex,

That is very odd. Are you sure that you tried to fit exactly the same
model? The first model reports "note: 54.fips omitted because of
collinearity", whereas the second reports "note: 54.state_id omitted
because of collinearity".

Cheers,
David

On 1 September 2013 23:27, Alex MacKay <mackay@uchicago.edu> wrote:
> Dear statalist,
>
> I ran into an interesting event in Stata yesterday. I had to increase
> the matsize for some of my regressions to work, and when I re-ran all
> of them, some of them that previously seemed fine now ran into an
> error (with the larger matsize). I've included the log output for both
> the regression with a matsize of 800 and one with a matsize of 10000.
>
> With a matsize of 800, all of the statistics and standard errors are
> reported. With a matsize of 10000, I get "Warning:  variance matrix is
> nonsymmetric or highly singular," standard errors are not reported,
> and the model degrees of freedom are reported as zero. You can see
> that I am running the exact same regression, as the estimated
> coefficient is the same and the same fixed effects are excluded. In
> addition, the root MSE changes as well.
>
> Any ideas on why the estimate of the variance matrix would change with
> a larger matsize when the first was nonbinding (only 599
> observations)?
>
> The regressions are run using -areg- in Stata 12 on a Unix server.
>
> Thanks,
> Alex
>
>
> //Matsize == 800
>
> note: 2599.week omitted because of collinearity
> note: 597.retailer_id omitted because of collinearity
> note: 866.retailer_id omitted because of collinearity
> note: 877.retailer_id omitted because of collinearity
> note: 9101.retailer_id omitted because of collinearity
> note: 54.fips omitted because of collinearity
> note: 3997.retailer_id omitted because of collinearity
> note: 4955.retailer_id omitted because of collinearity
> note: 7005.retailer_id omitted because of collinearity
> note: 7599.retailer_id omitted because of collinearity
>
> Linear regression, absorbing indicators           Number of obs   =        597
>                                                   F(  49,     45) =          .
>                                                   Prob > F        =          .
>                                                   R-squared       =     0.9256
>                                                   Adj R-squared   =     0.8695
>                                                   Root MSE        =     0.3085
>
>                       (Std. Err. adjusted for 46 clusters in clusterID)
> ------------------------------------------------------------------------------
>              |               Robust
>     ln_price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
> -------------+----------------------------------------------------------------
>          dummy |  -4.044072   3.152507    -1.28   0.206    -10.39355    2.305404
>
>
>
> //Matsize = 10000
>
> note: 2599.week omitted because of collinearity
> note: 597.retailer_id omitted because of collinearity
> note: 866.retailer_id omitted because of collinearity
> note: 877.retailer_id omitted because of collinearity
> note: 9101.retailer_id omitted because of collinearity
> note: 54.state_id omitted because of collinearity
> Warning:  variance matrix is nonsymmetric or highly singular
> note: 3997.retailer_id omitted because of collinearity
> note: 4955.retailer_id omitted because of collinearity
> note: 7005.retailer_id omitted because of collinearity
> note: 7599.retailer_id omitted because of collinearity
>
> Linear regression, absorbing indicators           Number of obs   =        597
>                                                   F(   0,     45) =          .
>                                                   Prob > F        =          .
>                                                   R-squared       =     0.9256
>                                                   Adj R-squared   =     0.8695
>                                                   Root MSE        =     0.2950
>
>                       (Std. Err. adjusted for 46 clusters in  clusterID)
> ------------------------------------------------------------------------------
>              |               Robust
>     ln_price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
> -------------+----------------------------------------------------------------
>          dummy |  -4.044072          .        .
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