[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
"Claire Kamp Dush" <ckamp-dush@ehe.osu.edu> |

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

Subject |
st: psmatch2/xtlogit fe/xtreg fe |

Date |
Thu, 11 Oct 2007 11:37:31 -0400 |

Dear Statalisters, I am having a problem attempting to conduct fixed effects regression with a matched sample obtained from psmatch2. Below is the detailed information about my problem. Sample: Large sample of mothers in married or cohabiting unions measured over three time points. They must be married or cohabiting at the second time point. Research question: Is there a difference in the impact of marital vs. cohabiting dissolution on mental health? Method: I run several models that all have the same issues. Below is an example. First I use probit to obtain the predicted probability that an individual is in the group that dissolved their cohabiting union at Wave 3. The sample here is all those who were cohabiting at wave 2. Predictors include several Wave 1 observed variables that are related to union stability and Wave 2 depression. Next, I use the predicted probability to do propensity score matching using psmatch2 (nearest neighbor with replacement, caliper 0.03) where the treated group is those that dissolve a cohabiting union by Wave 3 and the control group is those that are intact by Wave 3. The outcome is depression at Wave 3. I then create a dataset with just the treated and matched controls, the weight variable produced by psmatch2, and an indicator of union status at Waves 2 and 3. I put into this dataset also a variety of observed variables measured at Wave 2 and Wave 3 that also change over time, including their child's age, mother and father's employment status (dummies), mothers school status and whether she completed her education (dummies), mother's welfare receipt (dummy), total family income, total num of adults working in the household, whether the dad has ever been in jail, days the dad saw the child, whether the mother obtains a new partner (dummy), the length of time since separa! tion, and my outcome variable at Wave 2 and 3. I then convert this dataset to long. Note that I do this for each dependent variable separately - I have four measures of mental health- here I would like to discuss a continuous measure of depressive symptoms and a dichotomous measure of clinical depressed. Finally, I am attempting to run a fixed effects regression on the matched sample, so that my final estimate accounts for observed characteristics that do not change over time but distinguish those in cohabiting unions that dissolve and do not dissolve, observed characteristics that do change over time and may also account for a decline in mental health following a cohabiting dissolution, and unobserved characteristics that do not change over time. Here is where I run into the problems. Problem 1: When I attempt to run a fe xtlogit on the dummy of clinical depression, here is what I get an error "Multiple positive outcomes within groups encountered". This is for the baseline model with just the dichotomous measure of depressive symptoms (deplib) and the indicator of union status at each wave (cohdis) entered in the equation. I am getting this error because several groups do not experience a cohabitation dissolution? My code and output is listed below. Problem 2: When I attempt to run a fe xtreg on the continuous measure of depressive symptoms, I am not allowed to have a weight, so I cannot weight my controls by how many treated they matched to. I could try to re-run the analysis removing the "with replacement" option, or I could expand the data to have as many groups for each control as treated they match to. But, my question is, would my standard errors then be wrong? Is there a way to adjust my standard errors if I did this? Code for this is also below. Any help at all would be extremely appreciated, and if I am missing something huge as I am very new to fe regression and to propensity score matching, feel free to send me a reference. Thank you for reading this long e-mail, Claire Kamp Dush PROBLEM 1 CODE AND OUTPUT: foreach y in cohdis { foreach x in deplib { clear use "C:\Documents and Settings\ckamp-dush\Desktop\ff data for ncfr_long_ms_`y'_`x'.dta" xtset idnum wave di "*************************" di "`y'" di "`x'" xtlogit `x' `y' [iweight=_weight_`y'_`x'], fe or outreg2 using table_`y'_`x', bdec(2) alpha(0.001, 0.01, 0.05) eform replace xtlogit `x' `y' childage memployed mominschool momcompledu demployed djailever mdaysdadsawc mnewpart lengthseparated[iweight=_weight_`y'_`x'], fe or outreg2 using table_`y'_`x', bdec(2) alpha(0.001, 0.01, 0.05) eform xtlogit `x' `y' childage memployed mominschool momcompledu mwelfareusepastyr mtotinc mtotadultworking demployed djailever mdaysdadsawc mnewpart lengthseparated[iweight=_weight_`y'_`x'], fe or outreg2 using table_`y'_`x', bdec(2) alpha(0.001, 0.01, 0.05) eform } } panel variable: idnum (strongly balanced) time variable: wave, 2 to 3 delta: 1 unit note: multiple positive outcomes within groups encountered. note: 622 groups (1244 obs) dropped because of all positive or all negative outcomes. Iteration 0: log likelihood = -124.10619 Iteration 1: log likelihood = -123.27992 Iteration 2: log likelihood = -123.27585 Iteration 3: log likelihood = -123.27585 Conditional fixed-effects logistic regression Number of obs = 308 Group variable: idnum Number of groups = 154 Obs per group: min = 2 avg = 2.0 max = 2 LR chi2(1) = 15.46 Log likelihood = -123.27585 Prob > chi2 = 0.0001 ------------------------------------------------------------------------------ deplib | OR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- cohdis | 2.258065 .487155 3.78 0.000 1.479442 3.446471 ------------------------------------------------------------------------------ note: multiple positive outcomes within groups encountered. note: 674 groups (1044 obs) dropped because of all positive or all negative outcomes. Iteration 0: log likelihood = -67.689595 Iteration 1: log likelihood = -63.372241 Iteration 2: log likelihood = -63.315018 Iteration 3: log likelihood = -63.314907 Iteration 4: log likelihood = -63.314907 Conditional fixed-effects logistic regression Number of obs = 186 Group variable: idnum Number of groups = 93 Obs per group: min = 2 avg = 2.0 max = 2 LR chi2(10) = 38.34 Log likelihood = -63.314907 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ deplib | OR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- cohdis | 57.71542 92.04481 2.54 0.011 2.533913 1314.595 childage | 1.016448 .0143132 1.16 0.247 .9887784 1.044892 memployed | .3803397 .1602152 -2.29 0.022 .1665749 .868428 mominschool | .2570786 .130673 -2.67 0.008 .0949293 .6961958 momcompledu | 1.120824 .6749133 0.19 0.850 .3443339 3.648341 demployed | .3515572 .1518083 -2.42 0.015 .1508109 .8195192 djailever | .4047902 .226039 -1.62 0.105 .1354909 1.209344 mdaysdadsawc | 1.087415 .0485943 1.88 0.061 .9962238 1.186954 mnewpart | .1073525 .0897959 -2.67 0.008 .0208362 .5531041 lengthsepa~d | .9767578 .059858 -0.38 0.701 .8662101 1.101414 ------------------------------------------------------------------------------ note: multiple positive outcomes within groups encountered. note: 658 groups (987 obs) dropped because of all positive or all negative outcomes. Iteration 0: log likelihood = -57.304129 Iteration 1: log likelihood = -53.118765 Iteration 2: log likelihood = -53.02455 Iteration 3: log likelihood = -53.024308 Iteration 4: log likelihood = -53.024308 Conditional fixed-effects logistic regression Number of obs = 160 Group variable: idnum Number of groups = 80 Obs per group: min = 2 avg = 2.0 max = 2 LR chi2(13) = 39.51 Log likelihood = -53.024308 Prob > chi2 = 0.0002 ------------------------------------------------------------------------------ deplib | OR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- cohdis | 48.82699 83.27831 2.28 0.023 1.725313 1381.821 childage | 1.014492 .0162299 0.90 0.368 .9831758 1.046806 memployed | .2965949 .1438908 -2.51 0.012 .1146066 .7675694 mominschool | .2140359 .135176 -2.44 0.015 .0620732 .7380221 momcompledu | 1.46982 .9910594 0.57 0.568 .3920328 5.510691 mwelfareus~r | .5303944 .2423633 -1.39 0.165 .2165923 1.298837 mtotinc | 1.000015 .000013 1.13 0.257 .9999893 1.00004 mtotadultw~g | .9618368 .3848976 -0.10 0.923 .4390122 2.107299 demployed | .413399 .2286302 -1.60 0.110 .1398339 1.222155 djailever | .2964741 .1805884 -2.00 0.046 .0898468 .9782972 mdaysdadsawc | 1.066868 .0503679 1.37 0.170 .9725787 1.170299 mnewpart | .1997183 .188201 -1.71 0.087 .0314994 1.266288 lengthsepa~d | .9477549 .0726007 -0.70 0.484 .8156271 1.101287 ------------------------------------------------------------------------------ PROBLEM 2 CODE AND OUTPUT: foreach y in cohdis { foreach x in depc { clear use "C:\Documents and Settings\ckamp-dush\Desktop\ff data for ncfr_long_ms_`y'_`x'.dta" xtset idnum wave di "*************************" di "`y'" di "`x'" xtreg `x' `y' [iweight=_weight_`y'_`x'], fe outreg2 using table_`y'_`x', bdec(2) alpha(0.001, 0.01, 0.05) addstat (F, e(F)) replace xtreg `x' `y' childage memployed mominschool momcompledu demployed djailever mdaysdadsawc mnewpart lengthseparated [iweight=_weight_`y'_`x'], fe outreg2 using table_`y'_`x', bdec(2) alpha(0.001, 0.01, 0.05) addstat (F, e(F)) xtreg `x' `y' childage memployed mominschool momcompledu mwelfareusepastyr mtotinc mtotadultworking demployed djailever mdaysdadsawc mnewpart lengthseparated [iweight=_weight_`y'_`x'], fe outreg2 using table_`y'_`x', bdec(2) alpha(0.001, 0.01, 0.05) addstat (F, e(F)) } } panel variable: idnum (strongly balanced) time variable: wave, 2 to 3 delta: 1 unit ************************* cohdis depc iweight not allowed r(101); Claire M. Kamp Dush Assistant Professor Department of Human Development and Family Science The Ohio State University 171A Campbell Hall Phone: (614) 247-2126 http://ehe.osu.edu/#hd http://ipr.osu.edu/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: psmatch2/xtlogit fe/xtreg fe***From:*"Austin Nichols" <austinnichols@gmail.com>

- Prev by Date:
**Re: st: RE: re: missing dummy variable** - Next by Date:
**Re: st: RE: re: missing dummy variable** - Previous by thread:
**st: Controlling output in Results window and/or in log file** - Next by thread:
**Re: st: psmatch2/xtlogit fe/xtreg fe** - Index(es):

© Copyright 1996–2015 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |