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From |
Steve Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Sun, 4 Dec 2011 10:51:31 -0500 |

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: >> * 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/ * * 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**:**RE: st: Re: st: Re: st: Re: st: RE: Ratio of coefficients from two regressions and standard error.***From:*meenakshi beri <berimeenakshi@hotmail.com>

**References**:**st: Ratio of coefficients from two regressions and standard error.***From:*meenakshi beri <berimeenakshi@hotmail.com>

**st: RE: Ratio of coefficients from two regressions and standard error.***From:*"Silcocks, Paul" <Paul.Silcocks@liverpool.ac.uk>

**RE: st: RE: Ratio of coefficients from two regressions and standard error.***From:*meenakshi beri <berimeenakshi@hotmail.com>

**st: Re: st: RE: Ratio of coefficients from two regressions and standard error.***From:*Steve Samuels <sjsamuels@gmail.com>

**RE: st: Re: st: RE: Ratio of coefficients from two regressions and standard error.***From:*meenakshi beri <berimeenakshi@hotmail.com>

**st: Re: st: Re: st: RE: Ratio of coefficients from two regressions and standard error.***From:*Steve Samuels <sjsamuels@gmail.com>

**RE: st: Re: st: Re: st: RE: Ratio of coefficients from two regressions and standard error.***From:*meenakshi beri <berimeenakshi@hotmail.com>

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