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

From |
Douglas Lee Lauen <dlauen@unc.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: xtabond and lag structure problem |

Date |
Thu, 14 May 2009 12:01:09 -0400 |

We have an unbalanced multilevel longitudinal dataset with students nested in schools over time. We have variables of interest for both students (such as their position in the test score distribution) and school (such as their accountability scores). We have large N and small T (T=6 at most). We want to estimate the differential effects of a lagged school characteristic on kid’s test score through interactions of lagged distance to grade level and the school characteristic of interest. DV – score_t Exogenous – S_t-1 Endogenous test score_t-1 belowgl_t-1 abovegl_t-1 S_t-1*belowgl_t-1 S_t-1*abovegl_t-1 Where score_t is the kid’s test score at time t s_t-1 is whether the school that kid was enrolled in a time t had a particular characteristic in the prior year belowgl_t-1 is whether the kid was well below grade level in the prior year above gl_t-1 is whether the kid was well above grade level in the prior year ** Note that the interactions S_belowgl and S_abovegl involve both kid level and school level variables. We would like to run use the Arello-Bond estimator to run something like this: xtabond score l(1/1).sanc, lags(1) endogenous(belowgl abovegl S_belowgl S_abovegl, lag(1,.)) The problem is that we need to be able to utilize lags for the school-level variables that are based on the school's previous year status, but our dataset is student level yearly data. Therefore, if a student changes schools or appears in a school for the first time, the lag for a school variable will be based on the student's prior school, not the lag of the school’s prior measurement. This can be worked around by hard-coding the lagged variables in a school level dataset and then merging these variables back into the student level dataset. However, we would like to use the xtabond regression command but, as far as we can tell, the lagged variables cannot be hard-coded, so the lagged variables will not be correct to the school level. Thus, we need a way to specify the xtabond regression command to fit our multilevel data for the lags, or another work-around to code a new variable and "trick" Stata. Or will xtabond2, xtdpdsys, or xtdp work better for us? Thanks in advance for your thoughts! Doug Lauen UNC-Chapel Hill USA dlauen@unc.edu * * 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/

- Prev by Date:
**Re: st: offset() option in glm models** - Next by Date:
**st: Strange clustered data** - Previous by thread:
**st: DFBETAS after running glm model** - Next by thread:
**st: Strange clustered data** - Index(es):

© Copyright 1996–2016 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |