st: Matsize Increase Leads to Zero Model Degrees of Freedom

[Alex MacKay <mackay@uchicago.edu>]

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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