RE: st: Re: st: Re: st: Re: st: RE: Ratio of coefficients from two regressions and standard error‏.

[meenakshi beri <berimeenakshi@hotmail.com>]

Hello Steve,

Thanks a lot for your suggestions. I appreciate your time and effort.

Here is an issue:
1. I am using panel data and I found that xtreg is not supported by suest
2. If I use the previous code (as was suggested by you earlier) and create uhatmain as a temporary variable, I get the following error:
****************************************
bootstrap ratio1 =r(ratio1) , reps(100)cluster(hhidpn) idcluster(newhhid) nowarn : my_xtboot 
(running my_xtboot on estimation sample)
 : variable uhatmain not found
an error occurred when bootstrap executed my_xtboot
****************************************

Best,
Meenakshi
________________________________
Meenakshi Beri
Graduate Teaching Assistant
Department of Economics
Wayne State University
dy5651@wayne.edu


----------------------------------------
> Subject: st: Re: st: Re: st: Re: st: RE: Ratio of coefficients from two regressions and standard error‏.
> From: sjsamuels@gmail.com
> Date: Sun, 4 Dec 2011 10:51:31 -0500
> To: statalist@hsphsun2.harvard.edu
>
> You are welcome. After re-reading's Maarten's post, I recommend that you use temporary scalars (via -tempname-) and variables throughout the program, not permanent ones. Here the -bootstrap- automatically takes care of the cross-equation correlations. Many people would use -suest- for doing this. There are several advantages: 1) you can debug a complex program before -bootstrapping it; 2) you don't need to create scalars; 3) the equation names and coefficient names are used in the definition of the returned scalars, so there's less possibility of a mix-up.
>
> Here's the -suest- version.
>
> *********************************
> program myboot, rclass
> reg trunk length
> estimates store m1
>
> reg weight length
> estimates store m2
>
> suest m1 m2
> return scalar ratio = ///
> [m2_mean]length/[m1_mean]length
> end
> ***********************************
>
>
> The disadvantage is that -suest- cannot handle commands, like -stcox- that do not create a score variable for each observation.
>
>
> On Dec 4, 2011, at 1:39 AM, meenakshi beri wrote:
>
> Thanks a lot! I highly appreciate that.
>
> Best,Meenakshi
>
> ________________________________
> Meenakshi Beri
> Graduate Teaching Assistant
> Department of Economics
> Wayne State University
> dy5651@wayne.edu
>
>
> ----------------------------------------
> > Subject: st: Re: st: Re: st: RE: Ratio of coefficients from two regressions and standard error‏.
> > From: sjsamuels@gmail.com
> > Date: Sun, 4 Dec 2011 00:45:18 -0500
> > To: statalist@hsphsun2.harvard.edu
> >
> >
> >
> > Maarten Buis diagnosed this problem at http://www.stata.com/statalist/archive/2007-10/msg00201.html The second bootstrap replicate refuses to run because the variable "uhatmain" created by your -predict- statement already exists in the data set; as a result only one replicate is created. Maarten's suggestion: use a temporary variable. Or, drop "uhatmain" at the end of the program.
> >
> > Also drop all created scalars at the end of the program with a -scalar drop- statement; otherwise those from the last replicate will hang around.
> >
> >
> >
> > Steve
> >
> >
> >
> >
> >
> > On Dec 3, 2011, at 11:20 PM, meenakshi beri wrote:
> >
> > Hello Steve,
> >
> > Thanks for your reply and teaching me the way to estimate the ratio and standard error. I am using the same code (after modifying it as per panel data needs and multiple ratios needs) given by you, but I have been struggling with this insufficient observations error (otherwise my regressions are running fine without bootstrap). I have generated new identifier cluster variable also for the
> > bootstrapped panels, as well as generated the new time variable also (whatever information I got using previously existing statalist answers for such a situation, I tried that but nothing worked). Is there something I am doing wrong?
> >
> > I get the following error after bootstrap command:
> >
> > insufficient observations to compute bootstrap standard errors
> > no results will be saved
> > r(2000);
> >
> >
> > Here is my sample code:
> >
> >
> > program my_xtboot, rclass
> > xi: xtreg rwhappy hibp diab cancr l_inc_wealth hibp_wealth diab_wealth cancr_wealth hispanic if sample_black == 1, fe
> > scalar define b1 = _b[hibp_wealth]
> > scalar define b2 = _b[diab_wealth]
> > scalar define b3 = _b[cancr_wealth]
> > predict uhatmain, u
> >
> > xi: xtreg uhatmain l_inc_wealth hispanic, be
> > scalar define b8 = _b[l_inc_wealth]
> > return scalar ratio1 = b1/b8
> > return scalar ratio2 = b2/b8
> > return scalar ratio3 = b3/b8
> > end
> >
> > *generating new identiers for bootstrapping
> >
> > gen long newhhid = .
> > replace newhhid = hhidpn
> >
> > gen time2 = .
> > replace time2 = 1 if year == 1992
> > replace time2 = 2 if year == 1993
> > replace time2 = 3 if year == 1994
> > replace time2 = 4 if year == 1995
> >
> > * declaring new panel and time variable
> > tsset newhhid time2
> >
> > * getting rid of missing values
> > generate sample=1-missing(newhhid, time2, l_inc_wealth, rwhappy, hibp, diab, cancr, hispanic)
> > keep if sample
> >
> > *bootstrap ratio
> > bootstrap ratio1 =r(ratio1) , reps(100)cluster(hhidpn) idcluster(newhhid) nowarn: my_xtboot if sample_black == 1
> > estat bootstrap, all
> >
> > And here is what I get:
> >
> > bootstrap ratio1 =r(ratio1) , reps(100)cluster(hhidpn) idcluster(newhhid) nowarn: my_xtboot if sample_black == 1
> > (running my_xtboot on estimation sample)
> > : :
> > Bootstrap replications (100)
> > ----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
> > xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 50
> > xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 100
> > insufficient observations to compute bootstrap standard errors
> > no results will be saved
> > r(2000);
> >
> >
> > Best Regards,
> > Meenakshi
> > ________________________________
> > Meenakshi Beri
> > Graduate Teaching Assistant
> > Department of Economics
> > Wayne State University
> > dy5651@wayne.edu
> >
> >
> > ----------------------------------------
> >> Subject: st: Re: st: RE: Ratio of coefficients from two regressions and standard error‏.
> >> From: sjsamuels@gmail.com
> >> Date: Sat, 3 Dec 2011 16:23:19 -0500
> >> To: statalist@hsphsun2.harvard.edu
> >>
> >>
> >>
> >> Bootstrapping the ratio should give good results with fewer assumptions about the regression coefficients.
> >>
> >> Steve
> >> *************CODE BEGINS*************
> >> sysuse auto, clear
> >> capture program drop myboot
> >>
> >> program myboot, rclass
> >> reg trunk length
> >> scalar define b1 = _b[length]
> >>
> >> reg weight length
> >> scalar define b2 = _b[length]
> >>
> >> return scalar ratio = b2/b1
> >> end
> >>
> >> bootstrap ratio =r(ratio) , reps(40): myboot
> >> estat bootstrap, all
> >> **************CODE ENDS**************
> >>
> >> On Dec 2, 2011, at 10:44 AM, meenakshi beri wrote:
> >>
> >> Thanks for your reply. One more question -- how to use Fieller's theorem and derive confidence limits using stata in this case?
> >>
> >> Meenakshi Beri
> >> Graduate Teaching Assistant
> >> Department of Economics
> >> Wayne State University
> >> dy5651@wayne.edu
> >>
> >>
> >> From: Paul.Silcocks@liverpool.ac.uk
> >> To: statalist@hsphsun2.harvard.edu
> >> Subject: st: RE: Ratio of coefficients from two regressions and standard error‏.
> >> Date: Fri, 2 Dec 2011 09:23:04 +0000
> >>
> >> On the assumption that the two regression coefficient estimates have a Normal distribution, their ratio would have a Cauchy distribution (with no defined variance) if their correlation is zero. If the correlation is non-zero the exact distribution is complicated, though under certain conditions it tends to a Normal distribution.
> >>
> >> You'd be better off instead using Fieller's theorem to obtain confidence limits rather than estimating the standard error
> >>
> >> Paul Silcocks BM BCh, MSc , FRCPath, FFPH, CStat
> >> Senior statistician,
> >> Cancer Research UK Liverpool Cancer Trials Unit
> >> University of Liverpool
> >> Block C Waterhouse Building
> >> 1-3 Brownlow Street
> >> L69 3GL
> >>
> >> email: paul.silcocks@liverpool.ac.uk
> >> tel: 0151 7948802
> >> mob: 0794 983 2775
> >>
> >> -----Original Message-----
> >> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of meenakshi beri
> >> Sent: 02 December 2011 06:08
> >> To: statalist@hsphsun2.harvard.edu
> >> Subject: st: Ratio of coefficients from two regressions and standard error‏.
> >>
> >> Hello Statalist,
> >> I am running a fixed effects regression followed by an auxiliary regression to capture the coefficient of time invariant variables. I want to estimate the ratio of two coefficients from these two regressions respectively along with the standard error of the ratio. How can I estimate the ratio and standard error?
> >> Thanks,Meenakshi BeriWayne State University
> >> *
> >> * For searches and help try:
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> >>
> >> *
> >> * For searches and help try:
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> >> * http://www.ats.ucla.edu/stat/stata/
> >>
> >> *
> >> * For searches and help try:
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> >> * http://www.ats.ucla.edu/stat/stata/
> >>
> >>
> >> *
> >> * For searches and help try:
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> >> * http://www.ats.ucla.edu/stat/stata/
> >
> > *
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> >
> >
> > *
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>
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>
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