[R-SIG-Finance] R: [Fwd: R-SIG-Finance Digest, Vol 60, Issue 18]
Steven Archambault
archstevej at gmail.com
Tue May 19 21:16:30 CEST 2009
Oh, you are right. Here is the correct file. I sure have botched this query,
thanks for catching it Robert! Sorry for so many posts to the list.
Regards,
Steve
On Tue, May 19, 2009 at 12:19 PM, Robert Iquiapaza <rbali at ufmg.br> wrote:
> Stev,
>
> The data you provided is not complete, lagdlfdi and laglnstock2000 are not
> in the csv file
>
> Robert
>
> *From:* Steven Archambault <archstevej at gmail.com>
> *Sent:* Monday, May 18, 2009 5:06 PM
> *To:* Millo Giovanni <Giovanni_Millo at generali.com>
> *Cc:* r-sig-finance at stat.math.ethz.ch ; Yves Croissant<yves.croissant at let.ish-lyon.cnrs.fr>; Christian
> Kleiber <christian.kleiber at unibas.ch>
> *Subject:* Re: [R-SIG-Finance] R: [Fwd: R-SIG-Finance Digest, Vol 60,
> Issue 18]
>
> I just realized I used Robust in my Stata 9.2 analysis. When I remove this,
> the Chi-sq values are much closer to the values I get in R (but negative, as
> the consistent model must be listed first in a chi-sq calculation). However,
> with my own data I do get this positive definite error in Stata. Is this a
> result of unbalanced data? R doesn't give an error, so I am inclined to
> ignore it in Stata. I am posting my own results from R and Stata, and
> attaching the data as a csv.
>
> Thanks, hope I am not wasting too much of your time here.
>
> -Steve
>
> ###R-Output###
> > library("plm")
> >
> > fdi <- read.csv("C:/data/mydata.csv", na.strings=".")
> > fdiplm<-plm.data(fdi, index = c("id_code_id", "year"))
> series are constants and have been removed
> >
> > fdi_test<-(lfdi_2000~ lagdlfdi+ laglnstock2000+ lagtradegdp +lagdlgdp)
> >
> > fdi_test_fe <- plm(fdi_test, data=fdiplm, model="within")
> > fdi_test_re <- plm(fdi_test, data=fdiplm, model="random")
> >
> > summary (fdi_test_fe)
> Oneway (individual) effect Within Model
>
> Call:
> plm(formula = fdi_test, data = fdiplm, model = "within")
>
> Unbalanced Panel: n=149, T=3-27, N=2697
>
> Residuals :
> Min. 1st Qu. Median 3rd Qu. Max.
> -8.2100 -0.4760 0.0452 0.5670 4.8700
>
> Coefficients :
> Estimate Std. Error t-value Pr(>|t|)
> lagdlfdi 0.1564759 0.0180645 8.6621 < 2.2e-16 ***
> laglnstock2000 0.7621350 0.0246798 30.8809 < 2.2e-16 ***
> lagtradegdp 0.0178568 0.0025859 6.9055 5.003e-12 ***
> lagdlgdp 0.2601477 0.0427744 6.0818 1.188e-09 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Total Sum of Squares: 4606.7
> Residual Sum of Squares: 2938
> F-statistic: 361.237 on 4 and 2544 DF, p-value: < 2.22e-16
> > summary (fdi_test_re)
> Oneway (individual) effect Random Effect Model
> (Swamy-Arora's transformation)
>
> Call:
> plm(formula = fdi_test, data = fdiplm, model = "random")
>
> Unbalanced Panel: n=149, T=3-27, N=2697
>
> Effects:
> var std.dev share
> idiosyncratic 1.15487 1.07465 0.6617
> individual 0.59044 0.76840 0.3383
> theta :
> Min. 1st Qu. Median Mean 3rd Qu. Max.
> 0.3718 0.6700 0.7081 0.6955 0.7355 0.7401
>
> Residuals :
> Min. 1st Qu. Median Mean 3rd Qu. Max.
> -9.15000 -0.47900 0.07270 -0.00713 0.59800 3.95000
>
> Coefficients :
> Estimate Std. Error t-value Pr(>|t|)
> (Intercept) 16.7744214 0.1552868 108.0222 < 2.2e-16 ***
> lagdlfdi 0.1632388 0.0181005 9.0185 < 2.2e-16 ***
> laglnstock2000 0.8314432 0.0196444 42.3247 < 2.2e-16 ***
> lagtradegdp 0.0119453 0.0020737 5.7605 8.386e-09 ***
> lagdlgdp 0.2558009 0.0424599 6.0245 1.696e-09 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Total Sum of Squares: 9522.3
> Residual Sum of Squares: 3140.8
> F-statistic: 1367.42 on 4 and 2692 DF, p-value: < 2.22e-16
> >
> > phtest(fdi_test_re, fdi_test_fe)
>
> Hausman Test
>
> data: fdi_test
> chisq = 23.7021, df = 4, p-value = 9.164e-05
> alternative hypothesis: one model is inconsistent
>
>
> ###end R output###
>
> ###Stata 9.2 Output--canned###
> xtreg lfdi_2000 lagdlfdi laglnstock2000 lagtradegdp lagdlgdp, fe;
>
> Fixed-effects (within) regression Number of obs =
> 2697
> Group variable (i): id_code_id Number of groups =
> 149
>
> R-sq: within = 0.3622 Obs per group: min
> = 3
> between = 0.8234 avg =
> 18.1
> overall = 0.6998 max =
> 27
>
> F(4,2544) =
> 361.24
> corr(u_i, Xb) = 0.3536 Prob > F =
> 0.0000
>
>
> ------------------------------------------------------------------------------
> lfdi_2000 | Coef. Std. Err. t P>|t| [95% Conf.
> Interval]
>
> -------------+----------------------------------------------------------------
> lagdlfdi | .1564758 .0180645 8.66 0.000 .1210532
> .1918985
> laglnst~2000 | .762135 .0246798 30.88 0.000 .7137404
> .8105295
> lagtradegdp | .0178568 .0025859 6.91 0.000 .0127861
> .0229274
> lagdlgdp | .2601478 .0427744 6.08 0.000 .1762716
> .3440241
> _cons | 17.01131 .1701713 99.97 0.000 16.67762
> 17.345
>
> -------------+----------------------------------------------------------------
> sigma_u | .93048942
> sigma_e | 1.0746505
> rho | .42847396 (fraction of variance due to u_i)
>
> ------------------------------------------------------------------------------
> F test that all u_i=0: F(148, 2544) = 10.73 Prob > F =
> 0.0000
>
> . estimates store FIX, title(The FE) ;
>
> . xtreg lfdi_2000 lagdlfdi laglnstock2000 lagtradegdp lagdlgdp, re;
>
> Random-effects GLS regression Number of obs =
> 2697
> Group variable (i): id_code_id Number of groups =
> 149
>
> R-sq: within = 0.3606 Obs per group: min
> = 3
> between = 0.8402 avg =
> 18.1
> overall = 0.7128 max =
> 27
>
> Random effects u_i ~ Gaussian Wald chi2(4) =
> 2225.46
> corr(u_i, X) = 0 (assumed) Prob > chi2 =
> 0.0000
>
>
> ------------------------------------------------------------------------------
> lfdi_2000 | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
>
> -------------+----------------------------------------------------------------
> lagdlfdi | .1631662 .0180937 9.02 0.000 .1277032
> .1986291
> laglnst~2000 | .830845 .0196843 42.21 0.000 .7922645
> .8694255
> lagtradegdp | .011992 .0020779 5.77 0.000 .0079195
> .0160645
> lagdlgdp | .2558113 .0424486 6.03 0.000 .1726136
> .3390091
> _cons | 16.77702 .1556693 107.77 0.000 16.47191
> 17.08212
>
> -------------+----------------------------------------------------------------
> sigma_u | .77431228
> sigma_e | 1.0746505
> rho | .34173973 (fraction of variance due to u_i)
>
> ------------------------------------------------------------------------------
>
> . estimates store RAND, title(The RE) ;
>
> . hausman FIX RAND;
>
> ---- Coefficients ----
> | (b) (B) (b-B)
> sqrt(diag(V_b-V_B))
> | FIX RAND Difference S.E.
>
> -------------+----------------------------------------------------------------
> lagdlfdi | .1564758 .1631662 -.0066903 .
> laglnst~2000 | .762135 .830845 -.06871 .014887
> lagtradegdp | .0178568 .011992 .0058648 .0015393
> lagdlgdp | .2601478 .2558113 .0043365 .0052695
>
> ------------------------------------------------------------------------------
> b = consistent under Ho and Ha; obtained from
> xtreg
> B = inconsistent under Ha, efficient under Ho; obtained from
> xtreg
>
> Test: Ho: difference in coefficients not systematic
>
> chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
> = 22.94
> Prob>chi2 = 0.0001
> (V_b-V_B is not positive definite)
> ###End Stata 9.2####
>
>
>
>
>
>
>
> On Mon, May 18, 2009 at 12:26 PM, Steven Archambault <archstevej at gmail.com
> > wrote:
>
>> Giovani,
>>
>> Thank you so much for your comments. I am a bit new to R, and to these
>> mailing lists, so I apologize for being sparse on the details and examples.
>> I am using Stata 9.2, which might be the answer to my problem, as you
>> described. I have done quite a bit of internet searching, and did not read
>> anywhere about the use of a different method for calculating the chi-sq
>> value, so thanks for that.
>>
>> One more issue I have been thinking about. I am assuming your Plm
>> package knows that the FE is the consistient model, as the same results
>> arrive if the code is phtest(femod, remod) or phtest(remod, femod). The
>> order does matter in Stata.
>>
>> For complteness I am going to post my results using the same Grumfeld
>> dataset for both stata 9.2 (by hand calculation and canned procedure) and
>> R. I am using the Plm package version 1 1-2.
>>
>> Regards,
>> Steve
>>
>>
>>
>> ## begin Stata9.2 output##
>> xtreg inv value capital, robust re;
>>
>> Random-effects GLS regression Number of obs =
>> 200
>> Group variable (i): firmid Number of groups
>> = 10
>>
>> R-sq: within = 0.7668 Obs per group: min
>> = 20
>> between = 0.8196 avg =
>> 20.0
>> overall = 0.8061 max
>> = 20
>>
>> Random effects u_i ~ Gaussian Wald chi2(3) =
>> 77.70
>>
>> corr(u_i, X) = 0 (assumed) Prob > chi2 =
>> 0.0000
>>
>>
>> ------------------------------------------------------------------------------
>> | Robust
>> invest | Coef. Std. Err. z P>|z| [95% Conf.
>> Interval]
>>
>> -------------+----------------------------------------------------------------
>> value | .1097811 .0197587 5.56 0.000 .0710547
>> .1485076
>> capital | .308113 .0418387 7.36 0.000 .2261107
>> .3901153
>> _cons | -57.83441 24.67795 -2.34 0.019 -106.2023
>> -9.466507
>>
>> -------------+----------------------------------------------------------------
>>
>> sigma_u | 84.20095
>> sigma_e | 52.767964
>> rho | .71800838 (fraction of variance due to u_i)
>>
>> ------------------------------------------------------------------------------
>>
>> . matrix bfe=e(b);
>>
>> . matrix vfe=e(V);
>>
>> . estimates store remod;
>>
>> . xtreg inv value capital, robust fe;
>>
>> Fixed-effects (within) regression Number of obs =
>> 200
>> Group variable (i): firmid Number of groups
>> = 10
>>
>> R-sq: within = 0.7668 Obs per group: min
>> = 20
>> between = 0.8194 avg =
>> 20.0
>> overall = 0.8060 max
>> = 20
>>
>> F(2,188) =
>> 40.23
>>
>> corr(u_i, Xb) = -0.1517 Prob > F =
>> 0.0000
>>
>> ------------------------------------------------------------------------------
>> | Robust
>> invest | Coef. Std. Err. t P>|t| [95% Conf.
>> Interval]
>>
>> -------------+----------------------------------------------------------------
>> value | .1101238 .019378 5.68 0.000 .0718975
>> .1483501
>> capital | .3100653 .042795 7.25 0.000 .2256452
>> .3944854
>> _cons | -58.74393 23.37422 -2.51 0.013 -104.8534
>> -12.63449
>> -------------+----------------------------------------------------------------
>>
>> sigma_u | 85.732501
>> sigma_e | 52.767964
>> rho | .72525012 (fraction of variance due to u_i)
>>
>> ------------------------------------------------------------------------------
>>
>> ###Hausman by hand###
>>
>> . estimates store femod;
>>
>> . matrix vre=e(V);
>>
>> . matrix bre=e(b);
>>
>> . matrix bdif=bfe-bre;
>>
>> . matrix list bdif;
>>
>> bdif[1,3]
>> value capital _cons
>> y1 -.00034265 -.00195236 .90952273
>>
>> . matrix bdifp=bdif';
>>
>> . matrix dv=vfe-vre;
>>
>> . matrix dvi=inv(dv);
>>
>> . matrix list bdif;
>>
>> bdif[1,3]
>> value capital _cons
>> y1 -.00034265 -.00195236 .90952273
>>
>> . matrix list bdifp;
>>
>> bdifp[3,1]
>> y1
>> value -.00034265
>> capital -.00195236
>> _cons .90952273
>>
>> . matrix list dvi;
>>
>> symmetric dvi[3,3]
>> value capital _cons
>> value -7739.3615
>> capital 5808.2905 -5305.811
>> _cons 3.6641311 .98569198 -.00051157
>>
>> . matrix chisq=bdif*dvi*bdifp;
>>
>> . matrix list chisq;
>>
>> symmetric chisq[1,1]
>> y1
>> y1 -.01956929
>> ###Hausman canned###
>> . hausman femod remod;
>>
>> ---- Coefficients ----
>> | (b) (B) (b-B)
>> sqrt(diag(V_b-V_B))
>> | femod remod Difference S.E.
>>
>> -------------+----------------------------------------------------------------
>> value | .1101238 .1097811 .0003427 .
>> capital | .3100653 .308113 .0019524 .0089965
>> ------------------------------------------------------------------------------
>>
>> b = consistent under Ho and Ha; obtained from
>> xtreg
>> B = inconsistent under Ha, efficient under Ho; obtained from
>> xtreg
>>
>> Test: Ho: difference in coefficients not systematic
>>
>> chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B)
>> = -0.01 chi2<0 ==> model fitted on these
>> data fails to meet the asymptotic
>> assumptions of the Hausman test;
>> see suest for a generalized test ##
>> end Stata9.2 output ##
>>
>> ##begin Output R, using PLM 1.1-2###
>>
>> > test<-data(Grunfeld, package="Ecdat")
>> >
>> > fm <- inv~value+capital
>> > femod <- plm(fm, Grunfeld, model="within")
>> > summary(femod)
>> Oneway (individual) effect Within Model
>>
>> Call:
>> plm(formula = fm, data = Grunfeld, model = "within")
>>
>> Balanced Panel: n=10, T=20, N=200
>>
>> Residuals :
>> Min. 1st Qu. Median 3rd Qu. Max.
>> -184.000 -17.600 0.563 19.200 251.000
>>
>> Coefficients :
>> Estimate Std. Error t-value Pr(>|t|)
>> value 0.110124 0.011857 9.2879 < 2.2e-16 ***
>> capital 0.310065 0.017355 17.8666 < 2.2e-16 ***
>> ---
>> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>
>> Total Sum of Squares: 2244400
>> Residual Sum of Squares: 523480
>> F-statistic: 309.014 on 2 and 188 DF, p-value: < 2.22e-16
>>
>> > remod <- plm(fm, Grunfeld, model="random")
>> > summary(remod)
>> Oneway (individual) effect Random Effect Model
>> (Swamy-Arora's transformation)
>>
>> Call:
>> plm(formula = fm, data = Grunfeld, model = "random")
>>
>> Balanced Panel: n=10, T=20, N=200
>>
>> Effects:
>> var std.dev share
>> idiosyncratic 2784.458 52.768 0.282
>> individual 7089.800 84.201 0.718
>> theta: 0.86122
>>
>> Residuals :
>> Min. 1st Qu. Median 3rd Qu. Max.
>> -178.00 -19.70 4.69 19.50 253.00
>>
>> Coefficients :
>> Estimate Std. Error t-value Pr(>|t|)
>> (Intercept) -57.834415 28.898935 -2.0013 0.04536 *
>> value 0.109781 0.010493 10.4627 < 2e-16 ***
>> capital 0.308113 0.017180 17.9339 < 2e-16 ***
>> ---
>> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>
>> Total Sum of Squares: 2381400
>> Residual Sum of Squares: 548900
>> F-statistic: 328.837 on 2 and 197 DF, p-value: < 2.22e-16
>> > phtest(femod, remod)
>>
>> Hausman Test
>>
>> data: fm
>> chisq = 2.3304, df = 2, p-value = 0.3119
>> alternative hypothesis: one model is inconsistent
>>
>> ###end Plm###
>>
>>
>>
>>
>>
>> On Mon, May 18, 2009 at 6:01 AM, Millo Giovanni <
>> Giovanni_Millo at generali.com> wrote:
>>
>>> Dear Steve,
>>>
>>> I got your inquiry courtesy of Christian Kleiber, who brought it to our
>>> attention: please next time you post anything re a given package,
>>> include the maintainer's address. We cannot guarantee to parse all the
>>> daily digests of the R system!
>>>
>>> Your problem: can you please provide a reproducible example? Else it is
>>> difficult to help, not knowing your data, your results and even the
>>> Stata version you're using.
>>>
>>> In the following I replicate what you might have done on a well-known
>>> dataset.
>>>
>>> From Stata10, on the usual Grunfeld data taken from package "Ecdat":
>>>
>>> ## begin Stata10 output ##
>>> . xtreg inv value capital
>>>
>>> Random-effects GLS regression Number of obs =
>>> 200
>>> Group variable: firm Number of groups =
>>> 10
>>>
>>> R-sq: within = 0.7668 Obs per group: min =
>>> 20
>>> between = 0.8196 avg =
>>> 20.0
>>> overall = 0.8061 max =
>>> 20
>>>
>>> Random effects u_i ~ Gaussian Wald chi2(2) =
>>> 657.67
>>> corr(u_i, X) = 0 (assumed) Prob > chi2 =
>>> 0.0000
>>>
>>> ------------------------------------------------------------------------
>>> ------
>>> inv | Coef. Std. Err. z P>|z| [95% Conf.
>>> Interval]
>>> -------------+----------------------------------------------------------
>>> ------
>>> value | .1097811 .0104927 10.46 0.000 .0892159
>>> .1303464
>>> capital | .308113 .0171805 17.93 0.000 .2744399
>>> .3417861
>>> _cons | -57.83441 28.89893 -2.00 0.045 -114.4753
>>> -1.193537
>>> -------------+----------------------------------------------------------
>>> ------
>>> sigma_u | 84.20095
>>> sigma_e | 52.767964
>>> rho | .71800838 (fraction of variance due to u_i)
>>> ------------------------------------------------------------------------
>>> ------
>>>
>>> . estimates store remod
>>>
>>> . xtreg inv value capital, fe
>>>
>>> Fixed-effects (within) regression Number of obs =
>>> 200
>>> Group variable: firm Number of groups =
>>> 10
>>>
>>> R-sq: within = 0.7668 Obs per group: min =
>>> 20
>>> between = 0.8194 avg =
>>> 20.0
>>> overall = 0.8060 max =
>>> 20
>>>
>>> F(2,188) =
>>> 309.01
>>> corr(u_i, Xb) = -0.1517 Prob > F =
>>> 0.0000
>>>
>>> ------------------------------------------------------------------------
>>> ------
>>> inv | Coef. Std. Err. t P>|t| [95% Conf.
>>> Interval]
>>> -------------+----------------------------------------------------------
>>> ------
>>> value | .1101238 .0118567 9.29 0.000 .0867345
>>> .1335131
>>> capital | .3100653 .0173545 17.87 0.000 .2758308
>>> .3442999
>>> _cons | -58.74393 12.45369 -4.72 0.000 -83.31086
>>> -34.177
>>> -------------+----------------------------------------------------------
>>> ------
>>> sigma_u | 85.732501
>>> sigma_e | 52.767964
>>> rho | .72525012 (fraction of variance due to u_i)
>>> ------------------------------------------------------------------------
>>> ------
>>> F test that all u_i=0: F(9, 188) = 49.18 Prob > F =
>>> 0.0000
>>>
>>> . estimates store femod
>>>
>>> . hausman femod remod
>>>
>>> ---- Coefficients ----
>>> | (b) (B) (b-B)
>>> sqrt(diag(V_b-V_B))
>>> | femod remod Difference S.E.
>>> -------------+----------------------------------------------------------
>>> ------
>>> value | .1101238 .1097811 .0003427 .0055213
>>> capital | .3100653 .308113 .0019524 .0024516
>>> ------------------------------------------------------------------------
>>> ------
>>> b = consistent under Ho and Ha; obtained from
>>> xtreg
>>> B = inconsistent under Ha, efficient under Ho; obtained from
>>> xtreg
>>>
>>> Test: Ho: difference in coefficients not systematic
>>>
>>> chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B)
>>> = 2.33
>>> Prob>chi2 = 0.3119
>>>
>>> .
>>> ## end Stata10 output ##
>>>
>>> while from plm I get
>>>
>>> ## begin R putput ##
>>> > data(Grunfeld, package="Ecdat")
>>> > fm <- inv~value+capital
>>> >
>>> > femod <- plm(fm, Grunfeld)
>>> > remod <- plm(fm, Grunfeld, model="random")
>>> >
>>> > phtest(femod, remod)
>>>
>>> Hausman Test
>>>
>>> data: fm
>>> chisq = 2.3304, df = 2, p-value = 0.3119
>>> alternative hypothesis: one model is inconsistent
>>>
>>> ## end R output ##
>>>
>>> which, besides testifying to the goodness and parsimony of an
>>> object-oriented approach as far as screen output is concerned, looks
>>> rather consistent to me.
>>>
>>> I cannot but guess that the problem might stem from different RE
>>> estimates: previous versions of Stata used the Wallace-Hussein method by
>>> default for computing the variance of random effects. Now Stata uses
>>> Swamy-Arora, which has been the default of 'plm' since the beginning.
>>> Yet as plm() allows to choose, you can experiment with different values
>>> for the 'random.method' argument in order to see if you get the Stata
>>> result. I suggest you start by comparing the coefficient estimates you
>>> get from Stata and R: FE should be unambiguous, RE might vary as said
>>> above, and for good reason.
>>>
>>> You also didn't tell us whether your by-hand calculation agrees with
>>> phtest() output? (I guess it does not)
>>>
>>> Please let us know, possibly with a reproducible example and providing
>>> all the above info
>>> Giovanni
>>>
>>> PS please also make sure you're not using any VEEEEERY old version of
>>> 'plm' (prior to, say, 0.3): these had a bug in the p-value calculation
>>> which made it depend on the order of models compared (so that in the
>>> wrong case you got p.value=1).
>>>
>>> Giovanni Millo
>>> Research Dept.,
>>> Assicurazioni Generali SpA
>>> Via Machiavelli 4,
>>> 34132 Trieste (Italy)
>>> tel. +39 040 671184
>>> fax +39 040 671160
>>>
>>> > ----------------------------------------------------------------------
>>> > --
>>> >
>>> > Subject:
>>> > [R-SIG-Finance] Chi-sq Hausman test---R vs Stata
>>> > From:
>>> > Steven Archambault <archstevej at gmail.com>
>>> > Date:
>>> > Sun, 17 May 2009 23:14:13 -0600
>>> > To:
>>> > r-sig-finance at stat.math.ethz.ch
>>> >
>>> > To:
>>> > r-sig-finance at stat.math.ethz.ch
>>> >
>>> >
>>> > Hi all,
>>> >
>>> > I am running a panel time series regression testing Fixed Effects and
>>> > Random Effects. I decided to calculate the chi-sq value for the
>>> > Hausman test in both R (Phtest) and Stata. I get different results.
>>> > Even within Stata, calculating the Chi-sq value with the canned
>>> > procedure or by hand (using
>>> > matrices) gives different results. So, the question should come up
>>> there as
>>> > well.
>>> >
>>> > Does anybody have any insight on how to pick which results to use? I
>>> > guess the one that gives the result I want? Having different programs
>>> > give quite different values for the same tests is frustrating me. I'd
>>>
>>> > be interested in any feedback folks have!
>>> >
>>> > Thanks,
>>> > Steve
>>> >
>>> > [[alternative HTML version deleted]]
>>>
>>
>>
> ------------------------------
>
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