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From |
"Ariel Linden. DrPH" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
re: st: Propensity Score Matching |

Date |
Tue, 21 Feb 2012 10:08:30 -0800 |

See the responses to your questions below: " For the above table i don't understand why the mean for treated group is same for unmatched and matched sample" Because matching is by default an ATT estimator. As such the treated group's covariates will not change, but the controls' will be chosen to look as similar to those treated subjects on those covariates. Conversely, if you used a ATC estimator, the control group (unmatched and matched) would be identical and the matched treated subjects would chosen to look as similar to those controls. " and how do i get number of observations." -psmatch2- ( a user written program found on ssc) generates a new variable called _wt. If you run -tab treat _wt- (for 1:1 matching), you should see the count of treated vs controls who were matched. You may have to stipulate - if _support==1 - if there were those who fell off of common support. Is there any way i can identify which firm is matched against which firm? If you read the help file for psmatch2, you'll see that the authors state: _nk In the case of one-to-one and nearest-neighbors matching, for every treatment observation, it stores the observation number of the k-th matched control observation. Do not forget to sort by _id if you want to use the observation number (id) of for example the 1st nearest neighbor as in sort _id g x_of_match = x[_n1] Date: Mon, 20 Feb 2012 02:53:02 -0800 (PST) From: vikramfinavker <vikramfinavker@gmail.com> Subject: st: Propensity Score Matching Dear Statalisters, First i apologise for not being clear and I thank you all for your replies. I will try to explain what i want to do and where i am facing problem. I am using psmatch2 with nearest neighbours matching with replacement. Following is the command which i have used. psmatch2 allheg10 vw1zscore vw1lnmva vw1mvgroslev vw1quickratio vw1fsaleds d2-d11 yr_00-yr_10, n(1) And following is the results of above command Probit regression Number of obs = 3406 LR chi2(24) = 548.41 Prob > chi2 = 0.0000 Log likelihood = -1007.6135 Pseudo R2 = 0.2139 - ------------------------------------------------------------------------------ allheg10 | Coef. Std. Err. z P>|z| [95% Conf. Interval] - -------------+---------------------------------------------------------------- vw1zscore | .0075111 .002529 2.97 0.003 .0025543 .0124678 vw1lnmva | .3311453 .0249946 13.25 0.000 .2821568 .3801339 vw1mvgroslev | 1.457169 .1968683 7.40 0.000 1.071315 1.843024 vw1quickra~o | -.0910223 .0231545 -3.93 0.000 -.1364042 - -.0456404 vw1fsaleds | .0026704 .0009382 2.85 0.004 .0008316 .0045091 d2 | -.5984644 .2177925 -2.75 0.006 -1.02533 - -.171599 d3 | -.401803 .1907166 -2.11 0.035 -.7756007 - -.0280053 d4 | -.6046699 .1852172 -3.26 0.001 -.967689 - -.2416508 d5 | -.2739949 .1801271 -1.52 0.128 -.6270375 .0790477 d6 | -.1714531 .1972053 -0.87 0.385 -.5579683 .2150621 d7 | -.3407124 .190602 -1.79 0.074 -.7142855 .0328606 d8 | -.4092481 .1824948 -2.24 0.025 -.7669314 - -.0515648 d9 | -.5232287 .3189424 -1.64 0.101 -1.148344 .1018868 d11 | -.292377 .1768571 -1.65 0.098 -.6390105 .0542566 yr_00 | -.1229459 .131999 -0.93 0.352 -.381659 .1357673 yr_01 | .0223229 .1407681 0.16 0.874 -.2535775 .2982233 yr_02 | .1623231 .1470985 1.10 0.270 -.1259846 .4506308 yr_03 | -.1825395 .1357505 -1.34 0.179 -.4486057 .0835267 yr_04 | -.1473961 .1367151 -1.08 0.281 -.4153527 .1205606 yr_05 | -.1815719 .1393327 -1.30 0.193 -.454659 .0915152 yr_06 | -.0916688 .1448511 -0.63 0.527 -.3755717 .1922342 yr_07 | -.1135523 .1495029 -0.76 0.448 -.4065726 .1794681 yr_08 | -.0781585 .1607199 -0.49 0.627 -.3931636 .2368467 yr_09 | -.1666735 .1575016 -1.06 0.290 -.4753711 .1420241 _cons | -2.987349 .3787435 -7.89 0.000 -3.729673 - -2.245026 - ------------------------------------------------------------------------------ Now let me explain my variables. The dependent variable is Hedging dummy (i.e. 1 if a firm is uses foreign currency contracts or interest rate swaps and 0 otherwise). Other independent variables are determinants of hedging. THis regression will tell me what influences firms to hedge. So, Now i assume that the above method has created some kind of score which identifies firms that are similar on all independent factors but their choice of hedging. So, after identifying these firms i want to do following. I have different risk measures and i want to do a mean difference test for matched firms on these risk measures. I want to do like this; Control Gropup Treatment Group Variable N Mean Median Mean Median Diff in Means Risk 1 XYZ 0 0 0 0 0 Risk2 yxz 0 0 0 0 0 â€‹ Earlier some one told me to use pstest after running psmatch2. BY doing that i get the following results. . pstest vw1edf1jun vw1eqvol eqvolomvol - ---------------------------------------------------------------------------- | Mean %reduct | t-test Variable Sample | Treated Control %bias |bias| | t p>|t| - ------------------------+----------------------------------+---------------- vw1edf1jun Unmatched | .98219 2.3953 -31.0 | -7.61 0.000 Matched | .98219 2.31 -29.1 6.0 | -11.24 0.000 | | vw1eqvol Unmatched | .39455 .49404 -41.3 | -8.83 0.000 Matched | .39455 .44392 -20.5 50.4 | -9.13 0.000 | | eqvolomvol Unmatched | 2.3454 3.0729 -44.5 | -10.12 0.000 Matched | 2.3454 2.7199 -22.9 48.5 | -10.34 0.000 | | - ---------------------------------------------------------------------------- For the above table i don't understand why the mean for treated group is same for unmatched and matched sample and how do i get number of observations. Is there any way i can identify which firm is matched against which firm? If its still confusing please forgive me. I am referring the following paper to do this " The Effects of Derivatives on Firm risk and Value" by Sohnke Bartram, Gregory Brown and Jennifer Conrad. - -- Regards, Vikram Finavker * * 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/

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