Maximum likelihood estimation of endogenous switching and sample selection models for binary, ordinal, and count variables
AbstractStudying behavior in economics, sociology, and statistics often involves fitting models in which the response variable depends on a dummy variable- also known as a regime-switch variable- or in which the response variable is observed only if a particular selection condition is met. In either case, standard regression techniques deliver inconsistent estimators if unobserved factors that affect the re- sponse are correlated with unobserved factors that affect the switching or selection variable. Consistent estimators can be obtained by maximum likelihood estimation of a joint model of the outcome and switching or selection variable. This article describes a “wrapper” program, ssm, that calls gllamm (Rabe-Hesketh, Skrondal, and Pickles, GLLAMM Manual [University of California – Berkeley, Division of Bio- statistics, Working Paper Series, Paper No. 160]) to fit such models. The wrapper accepts data in a simple structure, has a straightforward syntax, and reports out- put that is easily interpretable. One important feature of ssm is that the log likelihood can be evaluated using adaptive quadrature (Rabe-Hesketh, Skrondal, and Pickles, Stata Journal 2: 1–21; Journal of Econometrics 128: 301–323). Copyright 2006 by StataCorp LP.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by StataCorp LP in its journal Stata Journal.
Volume (Year): 6 (2006)
Issue (Month): 3 (September)
Contact details of provider:
Web page: http://www.stata-journal.com/
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Miranda, Alfonso & Bratti, Massimiliano, 2006.
"Non-Pecuniary Returns to Higher Education: The Effect on Smoking Intensity in the UK,"
IZA Discussion Papers
2090, Institute for the Study of Labor (IZA).
- Massimiliano Bratti & Alfonso Miranda, 2010. "Non‐pecuniary returns to higher education: the effect on smoking intensity in the UK," Health Economics, John Wiley & Sons, Ltd., vol. 19(8), pages 906-920, August.
- Alfonso Miranda & Massimiliano Bratti, 2006. "Non-pecuniary returns to higher education: The effect on smoking intensity in the UK," Keele Economics Research Papers KERP 2006/04, Centre for Economic Research, Keele University.
- Wilde, Joachim, 2000. "Identification of multiple equation probit models with endogenous dummy regressors," Economics Letters, Elsevier, vol. 69(3), pages 309-312, December.
- Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2003. "Maximum likelihood estimation of generalized linear models with covariate measurement error," Stata Journal, StataCorp LP, vol. 3(4), pages 386-411, December.
- Heckman, James J, 1978.
"Dummy Endogenous Variables in a Simultaneous Equation System,"
Econometric Society, vol. 46(4), pages 931-59, July.
- James J. Heckman, 1977. "Dummy Endogenous Variables in a Simultaneous Equation System," NBER Working Papers 0177, National Bureau of Economic Research, Inc.
- Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
- Rabe-Hesketh, Sophia & Skrondal, Anders & Pickles, Andrew, 2005. "Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects," Journal of Econometrics, Elsevier, vol. 128(2), pages 301-323, October.
- Terza, Joseph V., 1998. "Estimating count data models with endogenous switching: Sample selection and endogenous treatment effects," Journal of Econometrics, Elsevier, vol. 84(1), pages 129-154, May.
- Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
- repec:tsj:stataj:v:2:y:2002:i:1:p:1-21 is not listed on IDEAS
- Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "Generalized multilevel structural equation modeling," Psychometrika, Springer, vol. 69(2), pages 167-190, June.
- Alfonso Miranda Caso Luengo, 2003.
"FIML estimation of an endogenous switching model for count data,"
United Kingdom Stata Users' Group Meetings 2003
07, Stata Users Group.
- Alfonso Miranda, 2004. "FIML estimation of an endogenous switching model for count data," Stata Journal, StataCorp LP, vol. 4(1), pages 40-49, March.
- Alfonso Miranda, 2005. "Estimation of ordinal response models, accounting for sample selection bias," United Kingdom Stata Users' Group Meetings 2005 11, Stata Users Group.
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page. reading list or among the top items on IDEAS.Access and download statistics
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum) or (Lisa Gilmore).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.