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From |
John Antonakis <John.Antonakis@unil.ch> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Adjusted R-squared comparison |

Date |
Wed, 06 Feb 2013 15:30:35 +0100 |

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 thestandard 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.NickOn Wed, Feb 6, 2013 at 12:30 PM, John Antonakis<John.Antonakis@unil.ch> 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 .................................................. 50Linear regression Number of obs= 74Replications = 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)andcheck the mean and SD manually.So, with these two values from both samples, I guess you could do at-testfor 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 itrepresents a specific metric in the field of accounting. Morespecifically,I use a model where returns are the dependent variable and earnings,alongwith the change in earnings, are the independent variables. In thismodelthe adjusted r-squared represents the value relevance of theearnings (thisis what I am trying to gauge). Therefore, I am obliged to use r2.Thank you for the procedure you mention John, but I had alreadytried itin the past. It is helpful, but only in a vague way. It does notprovide themean and the variance of r2, so I could use them to test thesignificance.For instance, the intervals almost always overlap when I use thismethod.That does not provide concrete evidence of statistical significance or non-significance. If I don't prove that there is (or there is not) astatistically significant difference, I cannot show whether mymetric (valuerelevance) has been altered between the two periods. 2013/2/6 John Antonakis <John.Antonakis@unil.ch> Can't agree more with you Nick. We should care more about havingconsistent estimators than high r-squares (i.e., Panagiotis, what Imeanhere is that we can still estimate the slope consistently even if wedon'thave a tight fitting regression line). So, I don't know why you areinterested in this comparison, Panagiotis. I would think you wouldbe moreinterested in comparing estimates, as in a Chow test (Chow, G. C.(1960).Tests of equality between sets of coefficients in two linearregressions.Econometrica, 28(3), 591-605). If you are using fixed-effectsmodels, youcan 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. NickOn Wed, Feb 6, 2013 at 10:37 AM, John Antonakis<John.Antonakis@unil.ch>wrote:I think that the only think you can do is to bootstrap the r-squaresandsee 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 twodifferent periods of time (each one spans for a period of years). Sofar,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 samemodel for each group of data. I've derived their adjusted r-squaredand Iwant to know if those two adjusted r-squared are significantlydifferentfrom each other. * * 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/ * * 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/ * * 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/* * 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/* * 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/ * * 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/

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**References**:**st: Adjusted R-squared comparison***From:*Panagiotis Manganaris <mangan@econ.auth.gr>

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