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From |
"carambas" <mcaramba@uni-bonn.de> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE:xtpcse vs. xtpcse, noconstant |

Date |
Tue, 10 Aug 2004 11:40:53 +0200 |

Thank you, Nick. Mine is a yield response model and since I am including mostly inputs as explanatory vars and some categorical dummies, so perhaps a model without const could work since no matter what I include, I get only good results with noconst. Cris Date: Mon, 9 Aug 2004 09:14:27 +0100 From: "Nick Cox" <n.j.cox@durham.ac.uk> Subject: st: RE: On the R^2, your starting point is now a prediction of zero, not a prediction of the mean response. In a much simpler case, below, dropping the constant gives a higher R-sq but a totally ludicrous model. Why then does the R-sq look so good? Because the predictions - -- which range from 11 to 30 mpg -- are much closer to the data than a prediction of 0 than the predictions of the first model to the mean of -mpg-. Your model is more complicated, and I can't see your data, but I guess that the same applies. If there is a really good reason, like a law of physics, to force predictions through the origin, then do it. (One can certainly improve on a linear regression of -mpg- on -weight-, a secondary point.) . sysuse auto, clear (1978 Automobile Data) . reg mpg weight Source | SS df MS Number of obs = 74 - -------------+------------------------------ F( 1, 72) = 134.62 Model | 1591.9902 1 1591.9902 Prob > F = 0.0000 Residual | 851.469256 72 11.8259619 R-squared = 0.6515 - -------------+------------------------------ Adj R-squared = 0.6467 Total | 2443.45946 73 33.4720474 Root MSE = 3.4389 - -------------------------------------------------------------------------- ---- mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval] - -------------+------------------------------------------------------------ ---- weight | -.0060087 .0005179 -11.60 0.000 -.0070411 -.0049763 _cons | 39.44028 1.614003 24.44 0.000 36.22283 42.65774 - -------------------------------------------------------------------------- ---- . reg mpg weight , noconst Source | SS df MS Number of obs = 74 - -------------+------------------------------ F( 1, 73) = 259.18 Model | 28094.8545 1 28094.8545 Prob > F = 0.0000 Residual | 7913.14549 73 108.399253 R-squared = 0.7802 - -------------+------------------------------ Adj R-squared = 0.7772 Total | 36008 74 486.594595 Root MSE = 10.411 - -------------------------------------------------------------------------- ---- mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval] - -------------+------------------------------------------------------------ ---- weight | .006252 .0003883 16.10 0.000 .0054781 .007026 - -------------------------------------------------------------------------- ---- Nick n.j.cox@durham.ac.uk chris carambas > I estimated an xtpcse model, and I observed that any of my > runs without > noconstant option has very low R-squared (i.e. 0.08--and this > is the same > result if I run it in xtreg which has no nocons option) but > with noconstant > option, all R-squared become high (i.e. from 0.80).Does anyone have > explanation for that? * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: looping across an unknown number of columns***From:*Jason Rachlin <Jason.Rachlin@morganstanley.com>

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