Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Tirthankar Chakravarty <tirthankar.chakravarty@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: a new robust variance-covariance estimator conditional on covariates |

Date |
Sun, 30 Oct 2011 07:17:36 -0700 |

Laszlo, Based on a very quick look, the standard errors they propose (based on their (2.8)) are not hard to compute at all - code is below. I have handled ties arbitrarily, but you can easily change this to your liking. However, as Tukey, "Far better an approximate answer to the right question, than the exact answer to the wrong question, which can always be made precise...." T ************************************************************** // Compute and compare the Abadie-Imbens-Zheng // covariance estimator sysuse auto, clear g ones = 1 g nmiss = !missing(mpg, weight, c.weight#c.weight, foreign) mata mata clear // a function to find the column index of the row minima real colvector function minindexmat(real matrix mA) { real colvector vReturn vReturn = J(rows(mA), 1, .) for(i = 1; i<= rows(mA); i++) { minindex(mA[i,.], 1, j=., w=.) if (!allof(w[,2], 1)) { /* uniqueness assumption false; arbitrarily pick one */ vReturn[i, 1] = j[1, 1] } else vReturn[i,1] = j } return(vReturn) } // regression mX = st_data( ., "ones weight c.weight#c.weight foreign", "nmiss") vY = st_data(., "mpg", "nmiss") vBeta = lusolve(mX'*mX, mX'*vY) // standard variance-covariance matrix vEps = vY - mX*vBeta dSigmaSqHat = colsum(vEps:^2)/(rows(mX) - cols(mX)) vSE = diagonal(sqrt(dSigmaSqHat*cholinv((mX'*mX)))) vT = vBeta:/vSE // t-statistics // White's variance-covariance matrix vSE2 = diagonal(sqrt(cholinv((mX'*mX))*mX'* diag((vEps):^2)*mX*cholinv((mX'*mX)))) vT2 = vBeta:/ vSE2 // t-statistics // Abadie-Imbens-Zheng variance-covariance matrix mA = J(rows(mX), 1, 1)#mX' mB = vec(mX')#J(1, rows(mX), 1) mC = ((mA - mB):^2)'*I(rows(mX))#J(cols(mX), 1, 1) _editvalue(mC, 0, .) vL = minindexmat(mC) vEpsL = vEps[vL, 1] mEpsL = vEpsL#J(1, cols(mX), 1) mXL = mX[vL, .] mXEpsL = mXL:*mEpsL mEps = vEps#J(1, cols(mX), 1) mXEps = mX:*mEps mDXEps = mXEps - mXEpsL vSE3 = diagonal(sqrt(0.5*cholinv(mX'*mX)* (mDXEps'*mDXEps)*cholinv(mX'*mX))) vT3 = vBeta:/vSE3 // t-statistics (vT, vT2, vT3) // standard, White, AIZ end ************************************************************** 2011/10/30 László Sándor <sandorl@gmail.com>: > Hi, > > I would like to get back to using the Abadie-Imbens-Zheng estimator. > (I did not do a great job with the exposition two days ago.) I would > be happy with an implementation under Stata 12 on unix (or any > previous version). > > Though David Card advised Guido to relabel the paper "Lower Your > Standard Errors by 10%!", the paper does make a convincing point that > a sample does not need to be treated as having random covariates in > many cases, e.g. when we have the full population (where "errors" or > noise can come from an imaginary superpopulation, but the covariates, > like which are the states of the US or the proportion of male in the > population, are arguably given) or when the subsample is not a random > subsample of the population anyway ("convenience samples"). These > arguments are more more common in the program evaluation literature, > where people often discuss the different estimands of population > treatment effects or only on the treated etc. > http://www.nber.org/papers/w17442 > > So irrespective of the claims of many "modern" graduate econometrics > textbooks, in some cases the usual Huber-White sandwich/robust > standard errors are excessively conservative (the immediate response > to White [1980] did see the point, like Chow [1984].). Abadie, Imbens > and Zheng suggest an improvement. > > I wonder what is a good approach to implement a new VCE in Stata and > how it is possible to use it across multiple commands. Or I need to > hack one ado file at a time, and use those? > > Any thoughts on this will be greatly appreciated. > > Thanks, > > Laszlo > > > László Sándor > PhD candidate in Economics > Harvard 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/ > -- Tirthankar Chakravarty tchakravarty@ucsd.edu tirthankar.chakravarty@gmail.com * * 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: a new robust variance-covariance estimator conditional on covariates***From:*László Sándor <sandorl@gmail.com>

**References**:**st: a new robust variance-covariance estimator conditional on covariates***From:*László Sándor <sandorl@gmail.com>

- Prev by Date:
**re: Re: st: Code from Stata Journal** - Next by Date:
**st: exporting a matrix to csv** - Previous by thread:
**st: a new robust variance-covariance estimator conditional on covariates** - Next by thread:
**Re: st: a new robust variance-covariance estimator conditional on covariates** - Index(es):