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RE: st: RE: Matsize and Estimation of the Variance Matrix in a Regression
From
Joe Canner <[email protected]>
To
"[email protected]" <[email protected]>
Subject
RE: st: RE: Matsize and Estimation of the Variance Matrix in a Regression
Date
Wed, 4 Sep 2013 16:07:47 +0000
How many levels are in week, retailer_id, state, product, and clusterID?
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Alex MacKay
Sent: Wednesday, September 04, 2013 12:03 PM
To: [email protected]
Subject: Re: st: RE: Matsize and Estimation of the Variance Matrix in a Regression
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 <[email protected]> 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: [email protected]
> [mailto:[email protected]] On Behalf Of Alex MacKay
> Sent: Wednesday, September 04, 2013 9:58 AM
> To: [email protected]
> 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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