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From | Alex MacKay <mackay@uchicago.edu> |
To | statalist@hsphsun2.harvard.edu |
Subject | st: Matsize Increase Leads to Zero Model Degrees of Freedom |
Date | Sun, 1 Sep 2013 16:27:38 -0500 |
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 . . * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/