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From |
meenakshi beri <[email protected]> |

To |
<[email protected]> |

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

Date |
Sun, 4 Dec 2011 06:39:48 +0000 |

```
Thanks a lot! I highly appreciate that.
Best,Meenakshi
________________________________
Meenakshi Beri
Graduate Teaching Assistant
Department of Economics
Wayne State University
[email protected]
----------------------------------------
> Subject: st: Re: st: Re: st: RE: Ratio of coefficients from two regressions and standard error.
> From: [email protected]
> Date: Sun, 4 Dec 2011 00:45:18 -0500
> To: [email protected]
>
>
>
> 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
> [email protected]
>
>
> ----------------------------------------
> > Subject: st: Re: st: RE: Ratio of coefficients from two regressions and standard error.
> > From: [email protected]
> > Date: Sat, 3 Dec 2011 16:23:19 -0500
> > To: [email protected]
> >
> >
> >
> > 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
> > [email protected]
> >
> >
> > From: [email protected]
> > To: [email protected]
> > 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: [email protected]
> > tel: 0151 7948802
> > mob: 0794 983 2775
> >
> > -----Original Message-----
> > From: [email protected] [mailto:[email protected]] On Behalf Of meenakshi beri
> > Sent: 02 December 2011 06:08
> > To: [email protected]
> > 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:
> > * http://www.stata.com/help.cgi?search
> > * http://www.stata.com/support/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/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/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/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/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/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/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
```

**Follow-Ups**:

**References**:**st: Ratio of coefficients from two regressions and standard error.***From:*meenakshi beri <[email protected]>

**st: RE: Ratio of coefficients from two regressions and standard error.***From:*"Silcocks, Paul" <[email protected]>

**RE: st: RE: Ratio of coefficients from two regressions and standard error.***From:*meenakshi beri <[email protected]>

**st: Re: st: RE: Ratio of coefficients from two regressions and standard error.***From:*Steve Samuels <[email protected]>

**RE: st: Re: st: RE: Ratio of coefficients from two regressions and standard error.***From:*meenakshi beri <[email protected]>

**st: Re: st: Re: st: RE: Ratio of coefficients from two regressions and standard error.***From:*Steve Samuels <[email protected]>

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