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


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: Re: st: Re: st: Re: st: Re: st: RE: Ratio of coefficients from two regressions and standard error‏.
Date   Sun, 4 Dec 2011 12:57:39 -0500

I'll bet you forgot the single quotes:
*********************************************
tempname uhatmain  // or tempvar uhatmain

predict `uhatmain', u

xi: xtreg `uhatmain' l_inc_wealth hispanic, be
********************************************

teve

On Dec 4, 2011, at 12:16 PM, meenakshi beri wrote:

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
>>> *
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>>> 
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>> 
>> *
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> 
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