000152283 001__ 152283
000152283 005__ 20180122225626.0
000152283 0247_ $$2Other$$ast0185
000152283 037__ $$a199-2016-2672
000152283 037__ $$a199-2016-3422
000152283 041__ $$aen_US
000152283 245__ $$aCentering and reference groups for estimates of fixed effects: Modifications to felsdvreg
000152283 260__ $$c2010
000152283 269__ $$a2010
000152283 270__ $$mkmihaly@rand.org$$pMihaly,   Kata
000152283 270__ $$mdanielm@rand.org$$pMcCaffrey,   Daniel F.
000152283 270__ $$mlockwood@rand.org$$pLockwood,   J. R.
000152283 270__ $$mtsass@fsu.edu$$pSass,   Tim R.
000152283 300__ $$a22
000152283 336__ $$aJournal Article
000152283 520__ $$aAvailability of large, multilevel longitudinal databases in various fields including labor economics (with workers and firms observed over time) and education research (with students and teachers observed over time) has increased the application of panel-data models with multiple levels of fixed-effects. Existing software routines for fitting fixed-effects models were not designed for applications in which the primary interest is obtaining estimates of any of the fixed-effects parameters. Such routines typically report estimates of fixed effects relative to arbitrary holdout units. Contrasts to holdout units are not ideal in cases where the fixed-effects parameters are of interest because they can change capriciously, they do not correspond to the structural parameters that are typically of interest, and they are inappropriate for empirical Bayes (shrinkage) estimation. We develop an improved parameterization of fixed-effects models using sum-to-zero constraints that provides estimates of fixed effects relative to mean effects within well-defined reference groups (e.g., all firms of a given type or all teachers of a given grade) and provides standard errors for those estimates that are appropriate for shrinkage estimation. We implement our parameterization in a Stata routine called felsdvregdm by modifying the felsdvreg routine designed for fitting highdimensional fixed-effects models. We demonstrate our routine with an example dataset from the Florida Education Data Warehouse.
000152283 542__ $$fLicense granted by Lisa Gilmore (lgilmore@stata.com) on 2013-07-10T14:01:51Z (GMT):

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000152283 650__ $$aResearch Methods/ Statistical Methods
000152283 6531_ $$afelsdvreg
000152283 6531_ $$afelsdvregdm
000152283 6531_ $$afixed effects
000152283 6531_ $$alinked employer–employee data
000152283 6531_ $$alongitudinal achievement data
000152283 700__ $$aMihaly, Kata
000152283 700__ $$aMcCaffrey, Daniel F.
000152283 700__ $$aLockwood, J. R.
000152283 700__ $$aSass, Tim R.
000152283 773__ $$d1st Quarter 2010$$jVolume 10$$kNumber 1$$o103$$q82$$tStata Journal
000152283 8564_ $$s297557$$uhttp://ageconsearch.umn.edu/record/152283/files/sjart_st0185.pdf
000152283 887__ $$ahttp://purl.umn.edu/152283
000152283 909CO $$ooai:ageconsearch.umn.edu:152283$$pGLOBAL_SET
000152283 912__ $$nSubmitted by Lisa Gilmore (lgilmore@stata.com) on 2013-07-10T14:05:08Z
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  Previous issue date: 2010
000152283 982__ $$gStata Journal>Volume 10, Number 1, 1st Quarter 2010
000152283 980__ $$a199