000119266 001__ 119266
000119266 005__ 20180122220509.0
000119266 0247_ $$2Other$$ast0123
000119266 037__ $$a199-2016-2332
000119266 037__ $$a199-2016-3102
000119266 041__ $$aen_US
000119266 245__ $$aMaximum likelihood and two-step estimation of an ordered-probit selection model
000119266 260__ $$c2007
000119266 269__ $$a2007
000119266 270__ $$mchiburis@princeton.edu$$pChiburis,   Richard
000119266 270__ $$mmlokshin@worldbank.org$$pLokshin,   Michael
000119266 300__ $$a16
000119266 336__ $$aJournal Article
000119266 520__ $$aWe discuss the estimation of a regression model with an ordered-probit selection rule. We have written a Stata command, oheckman, that computes two-step and full-information maximum-likelihood estimates of this model. Using Monte Carlo simulations, we compare the performances of these estimators under various conditions.
000119266 542__ $$fLicense granted by Lisa Gilmore (lgilmore@stata.com) on 2011-12-27T19:27:17Z (GMT):

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000119266 650__ $$aResearch Methods/ Statistical Methods
000119266 6531_ $$aoheckman
000119266 6531_ $$aselection bias
000119266 6531_ $$aordered probit
000119266 6531_ $$amaximum likelihood
000119266 700__ $$aChiburis, Richard
000119266 700__ $$aLokshin, Michael
000119266 773__ $$d2nd Quarter 2007$$jVolume 07$$kNumber 2$$o182$$q167$$tStata Journal
000119266 8564_ $$s314686$$uhttp://ageconsearch.umn.edu/record/119266/files/sjart_st0123.pdf
000119266 887__ $$ahttp://purl.umn.edu/119266
000119266 909CO $$ooai:ageconsearch.umn.edu:119266$$pGLOBAL_SET
000119266 912__ $$nSubmitted by Lisa Gilmore (lgilmore@stata.com) on 2011-12-27T19:29:11Z
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000119266 912__ $$nMade available in DSpace on 2011-12-27T19:29:13Z (GMT). No. of bitstreams: 1
sjart_st0123.pdf: 314686 bytes, checksum: 153e70db12c556bb1ee356819279bd44 (MD5)
  Previous issue date: 2007
000119266 982__ $$gStata Journal>Volume 7, Number 2, 2nd Quarter 2007
000119266 980__ $$a199