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Re: st: Adjusted R-squared comparison
From
John Antonakis <[email protected]>
To
[email protected]
Subject
Re: st: Adjusted R-squared comparison
Date
Wed, 06 Feb 2013 15:30:35 +0100
Yes, as far as I know, the Bootstrap SE is the SD of the mean of the
bootstrapped r-squares.
As for your bootstrap r2, as I said below you need one of:
e(r2_w) R-squared within model
e(r2_o) R-squared overall model
e(r2_b) R-squared between model
Best,
J.
__________________________________________
John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management
Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis
Associate Editor
The Leadership Quarterly
__________________________________________
On 06.02.2013 14:20, Panagiotis Manganaris wrote:
Just to be sure John, you mean that the bootstrap st.err. is the
standard deviation?
And Nick, do you say that if I use the following command:
bootstrap e(r2), seed(123) reps(50) : xtreg .......
I won't have reliable results?
On Wed, Feb 6, 2013 at 12:35 PM, Nick Cox wrote:
There is an extra dimension here. John's bootstrap example is a nice
simple example of a model applied to non-panel, non-time series data.
-bootstrap-ping panel data that are time series too is trickier, to
say the least.
Nick
On Wed, Feb 6, 2013 at 12:30 PM, John Antonakis
<[email protected]> wrote:
Hi Panagiotis:
In fact, the result you get is the mean and SD of the bootstrap.
Specifically:
sysuse auto
bootstrap e(r2), seed(123) reps(50) : reg price mpg weight
gives:
Bootstrap replications (50)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
Linear regression Number of obs
= 74
Replications = 50
command: regress price mpg weight
_bs_1: e(r2)
------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
| Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
_bs_1 | .2933891 .074451 3.94 0.000 .1474678
.4393104
------------------------------------------------------------------------------
.2933891 is the mean of the bootstrapped r-squares and .07215 is the SD.
If you wish to check this save the bootstrap estimates (using saving)
and
check the mean and SD manually.
So, with these two values from both samples, I guess you could do a
t-test
for the difference if this is what you are looking for.
Let's see what others might say.
Best,
J.
__________________________________________
John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management
Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis
Associate Editor
The Leadership Quarterly
__________________________________________
On 06.02.2013 12:57, Panagiotis Manganaris wrote:
Unfortunately Nick and John, I must use adj r-squared because it
represents a specific metric in the field of accounting. More
specifically,
I use a model where returns are the dependent variable and earnings,
along
with the change in earnings, are the independent variables. In this
model
the adjusted r-squared represents the value relevance of the
earnings (this
is what I am trying to gauge). Therefore, I am obliged to use r2.
Thank you for the procedure you mention John, but I had already
tried it
in the past. It is helpful, but only in a vague way. It does not
provide the
mean and the variance of r2, so I could use them to test the
significance.
For instance, the intervals almost always overlap when I use this
method.
That does not provide concrete evidence of statistical significance or
non-significance. If I don't prove that there is (or there is not) a
statistically significant difference, I cannot show whether my
metric (value
relevance) has been altered between the two periods.
2013/2/6 John Antonakis <[email protected]>
Can't agree more with you Nick. We should care more about having
consistent estimators than high r-squares (i.e., Panagiotis, what I
mean
here is that we can still estimate the slope consistently even if we
don't
have a tight fitting regression line). So, I don't know why you are
interested in this comparison, Panagiotis. I would think you would
be more
interested in comparing estimates, as in a Chow test (Chow, G. C.
(1960).
Tests of equality between sets of coefficients in two linear
regressions.
Econometrica, 28(3), 591-605). If you are using fixed-effects
models, you
can model the fixed-effects with dummies and then do a Chow test via
suest....see -help suest-.
Best,
J.
__________________________________________
John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management
Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis
Associate Editor
The Leadership Quarterly
__________________________________________
On 06.02.2013 11:40, Nick Cox wrote:
That's positive advice.
My own other idea is that adjusted R-squares are a lousy basis to
compare two models, even of the same kind. They leave out too much
information.
Nick
On Wed, Feb 6, 2013 at 10:37 AM, John Antonakis
<[email protected]>
wrote:
I think that the only think you can do is to bootstrap the r-squares
and
see
if their confidence intervals overlap.
To bootstrap you just do:
E.g.,
sysuse auto
bootstrap e(r2), seed(123) reps(1000) : reg price mpg weight
You will be interested in either:
e(r2_w) R-squared within model
e(r2_o) R-squared overall model
e(r2_b) R-squared between model
See help xtreg with respect to saved results.
Let's see if others have other ideas.
On 06.02.2013 10:22, Panagiotis Manganaris wrote:
I need to compare two adjusted r-squared of the same model for two
different periods of time (each one spans for a period of years). So
far,
I
have split my data in two groups, those that belong to the period
1998-2004
and those that belong to the period 2005-2011. Then I used xtreg on the
same
model for each group of data. I've derived their adjusted r-squared
and I
want to know if those two adjusted r-squared are significantly
different
from each other.
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