Bootstrap Standard Error 2sls
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Bootstrap Standard Error Stata
Last Week Last Month Show All Discussions only Photos only Videos only Links only Polls only Filtered by: Clear All bootstrap standard error r new posts Jenny Zha New Member Join Date: Feb 2015 Posts: 1 #1 Two stage least squares, bootstrapping for adjusted standard errors, but what is the "Observed Coef." in the output? 22
Bootstrap Standard Error Estimates For Linear Regression
Feb 2015, 21:45 In a two-step test, standard errors need to adjusted to account for generated regressor(s) (which are estimated from some first-stage reduced form). I use a non-parametric pairs bootstrap across the two stages to obtain the standard errors (standard deviation of the estimates obtained from the bootstrap reps). But what is the reported "Observed Coef." in the bootstrap output in a two-step procedure? In bootstrap standard error matlab a one-step regression, the coefficient from a vanilla OLS regress command and the observed coefficient form the bootstrapped regression are the same, because the bootstrapped output reports the original coefficient estimate. ("Although the average, theta_bar, of the bootstrapped estimates is used in calculating the standard deviation, it is not used as the estimated value of the statistic itself." from http://www.stata.com/manuals13/rbootstrap.pdf) ***start code************** use http://www.stata-press.com/data/r13/auto regress mpg weight gear bootstrap, reps(100) seed(1): regress mpg weight gear ***end code************** However, when I use bootstrap to run a 2SLS, the observed coefficient is not the original coefficient. Then, what is it, since it is not the average coefficient estimate from the bootstrap repetitions? For example, Observed Coef. of w_hat = -.0056947; while in contrast, if I were to use my original sample through the first and second stage regressions, the coefficient on weight_hat = -.0055945. Thank you in advance to whoever can explain what the "Observed Coef." is in a bootstrapped 2-step test. ***start code************** regress weight length trunk headroom predict weight_hat, xb regress mpg weight_hat gear ***end code************** ------------------------------------------------------------------------------ mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- weight_hat | -.0055945 .0007801 -7.17 0
RE: st: re: How to correct standard errors of a 2sls performed by hand ? Date Sat, 6 Feb 2010 00:28:56 +0100 Thank you kit ! In fact
Bootstrap Standard Error Formula
i use a logistic in the first step and ols in the
Bootstrap Standard Error Heteroskedasticity
second. I have one endogeneous binary variable. Therefore should i use bootstrap ? Is it sufficient to use bootstrap standard error in sas vce (bootstrap ) ? Anne-Sophie ---------------------------------------- > From: baum@bc.edu > Subject: st: re: How to correct standard errors of a 2sls performed by hand ? > Date: Fri, 5 Feb http://www.statalist.org/forums/forum/general-stata-discussion/general/885262-two-stage-least-squares-bootstrapping-for-adjusted-standard-errors-but-what-is-the-observed-coef-in-the-output 2010 17:11:11 -0500 > To: statalist@hsphsun2.harvard.edu > > <> > No need to do any bootstrapping if it is just linear regression: > > // how to fix 2SLS estimates done 'by hand' > sysuse auto, clear > ivreg2 price (weight = turn foreign) headroom, small > estat vce > di e(rmse) > mat v2sls = e(V) > // First http://www.stata.com/statalist/archive/2010-02/msg00308.html stage reg > qui reg weight turn foreign headroom > predict double what, xb > // Second stage reg > qui reg price what headroom > scalar rmsebyhand = e(rmse) > // the 'wrong' VCE, calculated from the instruments > mat vbyhand = e(V) > scalar dfk = e(df_r) > // the correct resids: orig regressors * second stage coeffs > g double eps2 = (price - _b[what]*weight - _b[headroom]*headroom - _b[_cons])^2 > qui su eps2 > // corrected RMSE, based on the correct resids > scalar rmsecorr = sqrt(r(sum) / dfk) > // corrected VCE, using the right s^2 > mat vcorr = (rmsecorr / rmsebyhand)^2 * vbyhand > mat li vcorr > // check to see that it equals the real 2SLS VCE > mat diff = v2sls - vcorr > mat li diff > > > Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html > An Introduction to Stata Programming | http://www.stata-press.com/books/isp.html > An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html > > > * > * For searches and help t
RE: st: RE: How to correct standard errors of a 2sls performed by hand ? Date Fri, 5 Feb http://www.stata.com/statalist/archive/2010-02/msg00303.html 2010 22:40:43 +0100 <> Not sure what you mean, you can open the file with any text editor, in spite of the file type ".do". Anyway: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3976195/ ******* * Program to return b for Heckman 2-step estimator of selection model program hecktwostep, eclass version 10.1 tempname b V tempvar xb capture drop standard error invmills probit dy $xlist predict `xb', xb generate invmills = normalden(`xb')/normprob(`xb') regress lny $xlist invmills matrix `b' = e(b) ereturn post `b' end // Following not included in book * Check preceding program by running once hecktwostep ereturn display * Bootstrap for Heckman two-step estimator using chapter 16 example bootstrap _b, bootstrap standard error reps(400) seed(10101) nodots nowarn: hecktwostep // Following not included in book * Check results heckman lny $xlist, select(dy = $xlist) twostep ******* HTH Martin -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Anne-Sophie Bergerès Sent: Freitag, 5. Februar 2010 22:22 To: statalist@hsphsun2.harvard.edu Subject: RE: st: RE: How to correct standard errors of a 2sls performed by hand ? Thank you for your answer Martin. However, the file mus13p1bootstrap.do does not work on my stata. Would you be able to copy the lines 116-139 for me please ? I thank you in advance for your help. Anne-Sophie ---------------------------------------- > From: martin.weiss1@gmx.de > To: statalist@hsphsun2.harvard.edu > Subject: st: RE: How to correct standard errors of a 2sls performed by hand ? > Date: Fri, 5 Feb 2010 21:53:30 +0100 > > > <> > > You may want to look at http://www.stata-press.com/data/mus/mus.zip, file > mus13p1bootstrap.do, lines 116-139. There, the authors demonstrate a > procedure
Health Search databasePMCAll DatabasesAssemblyBioProjectBioSampleBioSystemsBooksClinVarCloneConserved DomainsdbGaPdbVarESTGeneGenomeGEO DataSetsGEO ProfilesGSSGTRHomoloGeneMedGenMeSHNCBI Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedPubMed HealthSNPSRAStructureTaxonomyToolKitToolKitAllToolKitBookToolKitBookghUniGeneSearch termSearch Advanced Journal list Help Journal ListHealth Serv Resv.49(2); 2014 AprPMC3976195 Health Serv Res. 2014 Apr; 49(2): 731–750. Published online 2013 Nov 12. doi: 10.1111/1475-6773.12122PMCID: PMC3976195Computation of Standard ErrorsBryan E Dowd, William H Greene, and Edward C NortonDivision of Health Policy and Management, School of Public Health, University of Minnesota, Box 729 MMC, Minneapolis, MN 55455Stern School of Business, Kaufman Management Center, New York, NYDepartment of Health Management and Policy, Department of Economics, M3108 SPH II, Ann Arbor, MIAddress correspondence to Bryan E. Dowd, Ph.D., Division of Health Policy and Management, School of Public Health, University of Minnesota, Box 729 MMC, Minneapolis, MN 55455; e-mail: ude.nmu@100xdwod.Author information ► Copyright and License information ►Copyright © Health Research and Educational TrustSee the article "Afterword: Sample Design Is the Key" in volume 51 on page 1117.This article has been cited by other articles in PMC.AbstractObjectivesWe discuss the problem of computing the standard errors of functions involving estimated parameters and provide the relevant computer code for three different computational approaches using two popular computer packages.Study DesignWe show how to compute the standard errors of several functions of interest: the predicted value of the dependent variable for a particular subject, and the effect of a change in an explanatory variable on the predicted value of the