st: RE: Matsize and Estimation of the Variance Matrix in a Regression

[Joe Canner <jcanner1@jhmi.edu>]

Alex,

I'm no -areg- expert, but I would suggestion that if you want get more traction with this question, you should probably provide additional information, including:

1. The complete specification of your model
2. A description of the variables in your model (e.g., if categorical, how many levels)
3. Why you are increasing the -matsize- in the first place

I suspect that the model has some intrinsic problems that need to be fixed (perhaps something similar to what you have suggested) which will probably take care of the -matsize- issue (which is probably more of a symptom than a cause), but we would need to know more before offering a solution.

Regards,
Joe Canner
Johns Hopkins University School of Medicine

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Alex MacKay
Sent: Wednesday, September 04, 2013 9:58 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: Matsize and Estimation of the Variance Matrix in a Regression

Dear statalist,

I have run into an issue that when I increase the matsize, it can
cause a regression that previously ran with no warnings to return:
"Warning: variance matrix is nonsymmetric or highly singular."

It estimates the exact same coefficients across the board. I've put
the log for the first coefficient below. Notice the Warning in advance
of the output. With the larger matsize (10000), it does not estimate
standard errors, and the model degrees of freedom are zero.

I am using the areg command to absorb the variable product_id. Is it
possible that Stata is trying to generate a number of fixed effects
that exceed 800, the original matsize, and decides to drop the
product_id dummy variables? This may allow it to estimate standard
errors. If so, I think it should be reported as a bug.

Alex

(Note: I'm reposting in a way that may more clearly identify the
issues, now that I am familiar with replying).


//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]
-------------+----------------------------------------------------------------
         treatment |  -4.044072          .        .       .
.           .



//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]
-------------+----------------------------------------------------------------
         treatment |  -4.044072   3.152507    -1.28   0.206
-10.39355    2.305404
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