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

[Alex MacKay <mackay@uchicago.edu>]

1. The full specification is:

areg ln_price treatment postperiod treatmentXpostperiod
ln_unemployment ln_population ln_income price_index ///
     i.week i.retailer_id i.state, absorb(product) vce(cluster clusterID)

2. The fixed effects variables are stored as integers.

3. I'm increasing the matsize because I am running several
regressions, and for some I run into the issue: "matsize too small." I
re-ran all regressions, and for a few (like the one above) that did
not have the error, the results changed.

Alex

On Wed, Sep 4, 2013 at 10:24 AM, Joe Canner <jcanner1@jhmi.edu> wrote:
> 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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