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" <[email protected]>
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
[email protected]
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?
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